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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Journal Prestige (SJR): 1.547
Citation Impact (citeScore): 4
Number of Followers: 63  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1404
Published by IEEE Homepage  [228 journals]
  • Comparative Study of Real-Time Semantic Segmentation Networks in Aerial
           Images During Flooding Events

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      Authors: Farshad Safavi;Maryam Rahnemoonfar;
      Pages: 15 - 31
      Abstract: Real-time semantic segmentation of aerial imagery is essential for unmanned ariel vehicle applications, including military surveillance, land characterization, and disaster damage assessments. Recent real-time semantic segmentation neural networks promise low computation and inference time, appropriate for resource-limited platforms, such as edge devices. However, these methods are mainly trained on human-centric view datasets, such as Cityscapes and CamVid, unsuitable for aerial applications. Furthermore, we do not know the feasibility of these models under adversarial settings, such as flooding events. To solve these problems, we train the most recent real-time semantic segmentation architectures on the FloodNet dataset containing annotated aerial images captured after hurricane Harvey. This article comprehensively studies several lightweight architectures, including encoder–decoder and two-pathway architectures, evaluating their performance on aerial imagery datasets. Moreover, we benchmark the efficiency and accuracy of different models on the FloodNet dataset to examine the practicability of these models during emergency response for aerial image segmentation. Some lightweight models attain more than 60% test mIoU on the FloodNet dataset and yield qualitative results on images. This article highlights the strengths and weaknesses of current segmentation models for aerial imagery, requiring low computation and inference time. Our experiment has direct applications during catastrophic events, such as flooding events.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Axial Cross Attention Meets CNN: Bibranch Fusion Network for Change
           Detection

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      Authors: Lei Song;Min Xia;Liguo Weng;Haifeng Lin;Ming Qian;Binyu Chen;
      Pages: 32 - 43
      Abstract: In the previous years, vision transformer has demonstrated a global information extraction capability in the field of computer vision that convolutional neural network (CNN) lacks. Due to the lack of inductive bias in vision transformer, it requires a large amount of data to support its training. In the field of remote sensing, it costs a lot to obtain a significant number of high-resolution remote sensing images. Most existing change detection networks based on deep learning rely heavily on the CNN, which cannot effectively utilize the long-distance dependence between pixels for difference discrimination. Therefore, this work aims to use a high-performance vision transformer to conduct change detection research with limited data. A bibranch fusion network based on axial cross attention (ACABFNet) is proposed. The network extracts local and global information of images through the CNN branch and transformer branch, respectively, and then, fuses local and global features by the bidirectional fusion approach. In the upsampling stage, similar feature information and difference feature information of the two branches are explicitly generated by feature addition and feature subtraction. Considering that the self-attention mechanism is not efficient enough for global attention over small datasets, we propose the axial cross attention. First, global attention along the height and width dimensions of images is performed respectively, and then cross attention is used to fuse the global feature information along two dimensions. Compared with the original self-attention, the structure is more graphics processing unit friendly and efficient. Experimental results on three datasets reveal that the ACABFNet outperforms existing change detection algorithms.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • CNN, RNN, or ViT? An Evaluation of Different Deep Learning Architectures
           for Spatio-Temporal Representation of Sentinel Time Series

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      Authors: Linying Zhao;Shunping Ji;
      Pages: 44 - 56
      Abstract: Rich information in multitemporal satellite images can facilitate pixel-level land cover classification. However, what is the most suitable deep learning architecture for high-dimension spatio-temporal representation of remote sensing time series remains unclear. In this study, we theoretically analyzed the different mechanisms of the different deep learning structures, including the commonly used convolutional neural network (CNN), the high-dimension CNN [three-dimensional (3-D) CNN], the recurrent neural network, and the newest vision transformer (ViT), with regard to learning and representing the temporal information for spatio-temporal data. The performance of the different models was comprehensively evaluated on large-scale Sentinel-1 and Sentinel-2 time-series images covering the whole of Slovenia. First, the 3-D CNN, long short-term memory (LSTM), and ViT, which all have specific structures that preserve temporal information, can effectively extract the spatio-temporal information, with the 3-D CNN and ViT showing the best performance. Second, the performance of the 2-D CNN, in which the temporal information is collapsed, is lower than that of the 3-D CNN, LSTM, and ViT but outperforms the conventional methods. Thirdly,using both optical and synthetic aperture radar (SAR) images performs almost the same as using only optical images, indicating that the information that can be extracted from optical images is sufficient for land-cover classification. However, when optical images are unavailable, SAR imagescan provide satisfactorily classification results. Finally, the modern deep learning methods can effectively overcome the disadvantages in imaging conditions where parts of an image or images of some periods are missing. The testing data are available at gpcv.whu.edu.cn/data.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Adaptive Granulation-Based Convolutional Neural Networks With Single Pass
           Learning for Remote Sensing Image Classification

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      Authors: Sankar K. Pal;Dasari Arun Kumar;
      Pages: 57 - 70
      Abstract: Convolutional neural networks (CNNs) with the characteristics like spatial filtering, feed-forward mechanism, and back propagation-based learning are being widely used recently for remote sensing (RS) image classification. The fixed architecture of CNN with a large number of network parameters is managed by learning through a number of iterations, and, thereby increasing the computational burden. To deal with this issue, an adaptive granulation-based CNN (AGCNN) model is proposed in the present study. AGCNN works in the framework of fuzzy set theoretic data granulation and adaptive learning by upgrading the network architecture to accommodate the information of new samples, and avoids iterative training, unlike conventional CNN. Here, granulation is done both on the 2-D input image and its 1-D representative feature vector output, as obtained after a series of convolution and pooling layers. While the class-dependent fuzzy granulation on input image space exploits more domain knowledge for uncertainty modeling, rough set theoretic reducts computed on them select only the relevant features for input to CNN. During classification of unknown patterns, a new principle of roughness-minimization with weighted membership is adopted on overlapping granules to deal with the ambiguous cases. All these together improve the classification accuracy of AGCNN, while reducing the computational time significantly. The superiority of AGCNN over some state-of-the-art models in terms of different performance metrics is demonstrated for hyperspectral and multispectral images both quantitatively and visually.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Fine-Grained Object Detection in Remote Sensing Images via Adaptive Label
           Assignment and Refined-Balanced Feature Pyramid Network

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      Authors: Junjie Song;Lingjuan Miao;Qi Ming;Zhiqiang Zhou;Yunpeng Dong;
      Pages: 71 - 82
      Abstract: Object detection in high-resolution remote sensing images remains a challenging task due to the uniqueness of its viewing perspective, complex background, arbitrary orientation, etc. For fine-grained object detection in high-resolution remote sensing images, the high intra-class similarity is even more severe, which makes it difficult for the object detector to recognize the correct classes. In this article, we propose the refined and balanced feature pyramid network (RB-FPN) and center-scale aware (CSA) label assignment strategy to address the problems of fine-grained object detection in remote sensing images. RB-FPN fuses features from different layers and suppresses background information when focusing on regions that may contain objects, providing high-quality semantic information for fine-grained object detection. Intersection over Union (IoU) is usually applied to select the positive candidate samples for training. However, IoU is sensitive to the angle variation of oriented objects with large aspect ratios, and a fixed IoU threshold will cause the narrow oriented objects without enough positive samples to participate in the training. In order to solve the problem, we propose the CSA label assignment strategy that adaptively adjusts the IoU threshold according to statistical characteristics of oriented objects. Experiments on FAIR1M dataset demonstrate that the proposed approach is superior. Moreover, the proposed method was applied to the fine-grained object detection in high-resolution optical images of 2021 Gaofen challenge. Our team ranked sixth and was awarded as the winning team in the final.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Application of the LSTM Models for Baltic Sea Wave Spectra Estimation

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      Authors: Martin Simon;Sander Rikka;Sven Nõmm;Victor Alari;
      Pages: 83 - 88
      Abstract: This article proposes to apply long-short-term memory (LSTM) deep learning models to transform Sentinel-1 A/B interferometric wide (IW) swath image data into the wave density spectrum. Although spectral wave estimation methods for synthetic aperture radar data have been developed, similar approaches for coastal areas have not received enough attention. Partially, this is caused by the lack of high-resolution wave-mode data, as well as the nature of wind waves that have more complicated backscattering mechanisms compared to the swell waves for which the aforementioned methods were developed. The application of the LSTM model has allowed the transformation of the Sentinel-1 A/B IW one-dimensional image spectrum into wave density spectra. The best results in the test dataset led to the mean Pearson's correlation coefficient 0.85 for the comparison of spectra and spectra. The result was achieved with the LSTM model using $VV$ and $VH$ polarization spectra fed into the model independently. Experiments with LSTM neural networks that classify images into wave spectra with the Baltic Sea dataset demonstrated promising results in cases where empirical methods were previously considered.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Monitoring the Catastrophic Flood With GRACE-FO and Near-Real-Time
           Precipitation Data in Northern Henan Province of China in July 2021

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      Authors: Cuiyu Xiao;Yulong Zhong;Wei Feng;Wei Gao;Zhonghua Wang;Min Zhong;Bing Ji;
      Pages: 89 - 101
      Abstract: Zhengzhou and its surrounding areas, located in northern Henan Province, China, receive continuous extreme rainfall from July 17 to July 22, 2021. Northern Henan Province experiences extensive flash floods and urban floods, causing severe casualties and property damage. Understanding the variation of hydrologic features during this flood event could be valuable for future flood emergency response work and flood risk management. This study first demonstrates the rainstorm process based on near-real-time precipitation data from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0). To meet the temporal resolution required for monitoring this short-term flood event, reconstructed daily terrestrial water storage anomalies (TWSAs) based on GRACE and GRACE-FO data and CLDAS-V2.0 datasets are first introduced. The spatial and temporal evolution of the reconstructed daily TWSA is analyzed in the study area during this heavy rainfall event. We further employ a wetness index based on the reconstructed daily TWSA for flood warnings. Furthermore, the modeled soil moisture data and daily runoff data are used for flood monitoring. Results show that the reconstructed daily TWSA increases by 437.7 mm in just six days (from July 17 to July 22, 2021), with a terrestrial water storage increment of 9.4 km3. Compared with ITSG-Grace2018, the reconstructed daily TWSA has better potential for near-real-time flood monitoring for short-term events in a small region. The wetness index derived from reconstructed daily TWSA is potential for flood early warning.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Drone-Aided Detection of Weeds: Transfer Learning for Embedded Image
           Processing

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      Authors: Iaroslav Koshelev;Maxim Savinov;Alexander Menshchikov;Andrey Somov;
      Pages: 102 - 111
      Abstract: In this article, we address the problem of hogweed detection using a drone equipped with red, green, blue (RGB) and multispectral cameras. We study two approaches: 1) offline detection running on the orthophoto of the area scanned within the mission and 2) real-time scanning from the frame stream directly on the edge device performing the flight mission. We show that by fusing the information from an additional multispectral camera installed on the drone, there is an opportunity to boost the detection quality, which can then be preserved even with a single RGB camera setup by the introduction of an additional convolution neural network trained with transfer learning to produce the fake multispectral images directly from the RGB stream. We show that this approach helps either eliminate the multispectral hardware from the drone or, if only the RGB camera is at hand, boost the segmentation performance by the cost of slight increase in computational budget. To support this claim, we have performed an extensive study of network performance in simulations of both the real-time and offline modes, where we achieve at least 1.1% increase in terms of the mean intersection over union metric when evaluated on the RGB stream from the camera and 1.4% when evaluated on orthophoto data. Our results show that the proper optimization guarantees a complete elimination of the multispectral camera from the flight mission by adding a preprocessing stage to the segmentation network without the loss of quality.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A Back Propagation Neural Network-Based Radiometric Correction Method
           (BPNNRCM) for UAV Multispectral Image

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      Authors: Yin Zhang;Qingwu Hu;Hailong Li;Jiayuan Li;Tiancheng Liu;Yuting Chen;Mingyao Ai;Jianye Dong;
      Pages: 112 - 125
      Abstract: Radiometric correction is one of the most important preprocessing parts of unmanned aerial vehicle (UAV) multispectral remote sensing data analysis and application. In this article, a back propagation (BP) neural network-based radiometric correction method (BPNNRCM) considering optimal parameters was proposed. First, we used different UAV multispectral sensors (K6 equipped on the DJI M600, D-MSPC2000 equipped on the FEIMA D2000) to collect training, validation, testing and cross-validation data. Second, the radiometric correction results of BP neural network with different input variables and hidden layer node number were compared to select the best combination of input parameters and hidden layer node number. Finally, the radiometric correction accuracy and robustness of BP neural network considering the optimal parameters were verified. When the number of nodes in the input layer was five (digital number, UAV sensor height, wavelength, solar altitude angle, and temperature) and the number of nodes in the hidden layer was eight, the BP neural network had the best comprehensive performance in training time of train set and accuracy of validation/test set. In the aspect of accuracy and robustness, the absolute errors of test and cross-validation images' surface reflectance obtained by the BPNNRCM were all less than 0.054. The BPNNRCM had smaller average absolute error (0.0141), mean squared error (0.0003), mean absolute error (0.0141) and mean relative error (7.1%) comparing with empirical line method and radiative transfer model. In general, the research results of this article prove the feasibility and prospect of BPNNRCM for radiometric correction of UAV multispectral images.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • An Improved Imaging Algorithm for HRWS Space-Borne SAR Data Processing
           Based on CVPRI

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      Authors: Yanan Guo;Pengbo Wang;Xinkai Zhou;Tao He;Jie Chen;
      Pages: 126 - 140
      Abstract: In space-borne synthetic aperture radar (SAR), the sliding spotlight mode can acquire images with both high-resolution and wide-swath in azimuth direction. Due to the significant two-dimensional spatial variance of Doppler parameters, the traditional imaging algorithms based on the conventional range models is not available. In this article, the strategy of continuously varying pulse repetition interval (CVPRI) is likely to lead to a novel approach to dealing with the azimuth variance problem to realize high-resolution wide-swath (HRWS) imaging in azimuth direction for sliding spotlight SAR. First, the eighth-order Taylor expansion of the modified equivalent squint range model (MESRM-TE8) is adopted, and the accuracy of MESRM-TE8 is accordingly explained. Then, the properties of the spatial variance for the MESRM-TE8 are analyzed in detail, based on which the strategy of CVPRI is given theoretically to eliminate the azimuth variance. An improved imaging algorithm based on CVPRI is subsequently proposed to address the azimuthal-variant Doppler parameters and realize a batch data processing of a large scene in azimuth frequency domain. The extended scaling method is integrated in this algorithm to uniformly compensate cubic phase modulation introduced by CVPRI and circumvent azimuth time folding caused by subaperture processing in the focused image. Finally, the effectiveness of the CVPRI strategy and the proposed algorithm is demonstrated by the simulation results.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • OFFS-Net: Optimal Feature Fusion-Based Spectral Information Network for
           Airborne Point Cloud Classification

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      Authors: Peipei He;Kejia Gao;Wenkai Liu;Wei Song;Qingfeng Hu;Xingxing Cheng;Shiming Li;
      Pages: 141 - 152
      Abstract: Airborne laser scanning (ALS) point cloud classification is a necessary step for understanding 3-D scenes and their applications in various industries. However, the classification accuracy and efficiency are low: 1) point cloud classification methods lack effective filtering of the large number of traditional features, 2) significant category imbalance and coordinate scale problems in ALS point cloud classification. To address these problems, this article proposes an airborne LiDAR point cloud classification method based on deep learning network with optimal feature fusion-based spectral information. This method involves the following steps: First, multiscale point cloud features are extracted, and random forest method is used to filter the features, while spectral information is fused to obtain a point cloud feature dataset with less but better data. Second, to adapt to the characteristics of the airborne point cloud, the improved RandLA-Net can simultaneously retain the advantages of random sampling and learn deeper semantic information by fusing the constructed point cloud features with the local feature aggregation module in the network. Third, four fusion models are constructed to verify the effectiveness of the optimal feature fusion-based spectral information network (OFFS-Net) model for airborne point cloud classification. Last, these models are trained and tested on Vaihingen 3-D dataset. The OFFS-Net achieves overall accuracy score of 84.9% and F1-score of 72.3%, which are better than the mainstream methods. This also validates that the proposed OFFS-Net point cloud classification method, based on the advantages of geometric feature and spectral information is excellent.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Spatial and Temporal Evolution of Ground Subsidence in the Beijing Plain
           Area Using Long Time Series Interferometry

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      Authors: Yueze Zheng;Junhuan Peng;Xue Chen;Cheng Huang;Pinxiang Chen;Sen Li;Yuhan Su;
      Pages: 153 - 165
      Abstract: Due to the overexploitation of water resources, ground subsidence is becoming increasingly problematic in Beijing, China's political, economic, and cultural capital. This article aims to investigate the relationship between ground subsidence and changes in groundwater depth, and water supply from a long-term point of view. Multisource synthetic aperture radar (SAR) data using the interferometric SAR (InSAR) technique were adopted in this research, combined with a set of leveling and ground subsidence data in the Beijing Plain area from 2003 to 2020. The InSAR results demonstrate that ground subsidence in the plain area increased steadily from 2003 to 2015, expanding from sporadic to continuous laminar dispersion and producing five major subsidence centers. The South-to-North Water Diversion Project (SNWDP) that was completed in 2008 and 2015 considerably reduced the demand for groundwater supply in the Beijing Plain area. Since then, the groundwater level depth has continued to increase. However, since 2016, the ground subsidence rate has dramatically slowed down. The obtained results showed that, thanks to the SNWDP, which resulted in a decline in groundwater exploitation and an increase in renewable water recycling, the ground subsidence in Beijing's plain area has been effectively managed.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Panchromatic and Hyperspectral Image Fusion: Outcome of the 2022 WHISPERS
           Hyperspectral Pansharpening Challenge

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      Authors: Gemine Vivone;Andrea Garzelli;Yang Xu;Wenzhi Liao;Jocelyn Chanussot;
      Pages: 166 - 179
      Abstract: This article presents the scientific outcomes of the 2022 Hyperspectral Pansharpening Challenge organized by the 12th IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (IEEE WHISPERS 2022). The 2022 Hyperspectral Pansharpening Challenge aims at fusing a panchromatic image with hyperspectral data to get a high spatial resolution hyperspectral cube with the same spatial resolution of the panchromatic image while preserving the spectral information of hyperspectral data. Four datasets acquired by the PRISMA mission owned and managed by the Italian Space Agency have been prepared for participants. They are made available for the benefit of the scientific community. Each dataset contains a panchromatic image and a hyperspectral cube with different spatial resolutions. More than 100 registrations have been received for the event. Four teams submitted their outcomes. Since no team actually outperformed the baseline provided by the organizers, the challenge was declared inconclusive and no winner was recognized.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Shadow Pattern-Enhanced Building Height Extraction Using
           Very-High-Resolution Image

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      Authors: Xiran Zhou;Soe W. Myint;
      Pages: 180 - 190
      Abstract: Building height is valuable for a variety of foci in urban studies. The traditional field investigations are not practical for the updates of massive building height in a large-scale urban area. Given the relationship between building structures and their shadow sizes, the building shadow becomes practical for estimating its corresponding building height when its geometrical shape is visible in newly emerging very-high-resolution (VHR) images. However, the shadow shape of different buildings might vary significantly, posing a great challenge to determining the edge of shadow useful for predicting building height. This study proposes a shadow pattern classification system (ShadowClass) to summarize the varied shadow shapes into a number of pattern categories and employ a cutting-edge CNN model to classify the extracted shadows into a pattern for automatically determining the edge of a building shadow being useful for building height estimation. We integrated the proposed approach into two branches of the state-of-the-art approaches: shadow-based building height estimation with open cyberinfrastructure and shadow-based building height estimation with VHR image. The experimental results proved that the proposed method could be a practical solution for single and isolated buildings that have their complete shadow shape.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Solid Waste Detection in Cities Using Remote Sensing Imagery Based on a
           Location-Guided Key Point Network With Multiple Enhancements

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      Authors: Huifang Li;Chao Hu;Xinrun Zhong;Chao Zeng;Huanfeng Shen;
      Pages: 191 - 201
      Abstract: Solid waste is a widespread problem that is having a negative effect on the global environment. Owing to the ability of macroscopic observation, it is reasonable to believe that remote sensing could be an effective way to realize the detection and monitoring of solid waste. Solid waste is usually a mixture of various materials, with a randomly scattered distribution, which brings great difficulty to precise detection. In this article, we propose a deep learning network for solid waste detection in urban areas, aiming to realize the fast and automatic extraction of solid waste from the complicated and large-scale urban background. A novel dataset for solid waste detection was constructed by collecting 3192 images from Google Earth (with a resolution from 0.13 to 0.52 m), and then a location-guided key point network with multiple enhancements (LKN-ME) is proposed to perform the urban solid waste detection task. The LKN-ME method uses corner pooling and central convolution to capture the key points of an object. The location guidance is realized through constraining the key point locations situated of the annotated bounding box of an object. Multiple enhancements, including data mosaicing, an attention enhancement, and path aggregation, are integrated to improve the detection accuracy. The results show that the LKN-ME method can achieve a state-of-the-art AR100(the average recall computed over 100 detections per image) of 71.8% and an average precision of 44.0% for the DSWD dataset, outperforming the classic object detection methods in solving the solid waste detection problem.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Baseline-Based Soil Salinity Index (BSSI): A Novel Remote Sensing
           Monitoring Method of Soil Salinization

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      Authors: Zhimei Zhang;Yanguo Fan;Aizhu Zhang;Zhijun Jiao;
      Pages: 202 - 214
      Abstract: Soil salinization leads to dehydration of plants, which seriously threatens ecologically sustainable development and food security guarantee. In the complex and diverse coastal wetland environment, the impervious surface and bare soil have similar spectral features with salinized soil, which make it difficult for traditional satellite data and algorithms to accurately and timely monitor the small surface features of salinization. This article presents a baseline-based soil salinity index (BSSI) for soil salinization monitoring using medium-resolution data. In BSSI, we construct a virtual salinization baseline by connecting the near-infrared (NIR) band and the short-wave infrared-2 (SWIR2) band to enhance the spectral feature of salinized soils which border on the impervious surface. In addition, we calculate the distance between the short-wave infrared-1 (SWIR1) band and the virtual salinization baseline as the BSSI, which can effectively improve the stability of salinity inversion for different soils. Through data comparison and model simulations, BSSI has shown advantages over a series of the traditional salinization spectral indices (SSIs). The results show that the saline soil extraction accuracy of BSSI exceeds 85% and the correlation coefficient of the BSSI and the degree of soil salinization exceeds 0.90. Since the related spectral bands, such as NIR, SWIR1, and SWIR2, are available on many existing satellite sensors such as Landsat TM/ETM+, OLI, and sentinel 2, the BSSI concept can be extended to establish long-term records for soil salinization monitoring.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • SelfS2: Self-Supervised Transfer Learning for Sentinel-2 Multispectral
           Image Super-Resolution

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      Authors: Xiao Qian;Tai-Xiang Jiang;Xi-Le Zhao;
      Pages: 215 - 227
      Abstract: The multispectral image captured by the Sentinel-2 satellite contains 13 spectral bands with different resolutions, which may hider some of the subsequent applications. In this article, we design a novel method to super-resolve 20- and 60-m coarse bands of the S2 images to 10 m, achieving a complete dataset at the 10-m resolution. To tackle this inverse problem, we leverage the deep image prior expressed by the convolution neural network (CNN). Specifically, a plain ResNet architecture is adopted, and the 3-D separable convolution is utilized to better capture the spatial–spectral features. The loss function is tailored based on the degradation model, enforcing the network output obeying the degradation process. Meanwhile, a network parameter initialization strategy is designed to further mine the abundant fine information provided by existing 10-m bands. The network parameters are inferred solely from the observed S2 image in a self-supervised manner without involving any extra training data. Finally, the network outputs the super-resolution result. On the one hand, our method could utilize the high model capacity of CNNs and work without large amounts of training data required by many deep learning techniques. On the other hand, the degradation process is fully considered, and each module in our work is interpretable. Numerical results on synthetic and real data illustrate that our method could outperform compared state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Monitoring the Spatiotemporal Distribution of Invasive Aquatic Plants in
           the Guadiana River, Spain

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      Authors: Elena C. Rodríguez-Garlito;Abel Paz-Gallardo;Antonio Plaza;
      Pages: 228 - 241
      Abstract: Monitoring the spatiotemporal distribution of invasive aquatic plants is a challenge in many regions worldwide. One of the most invasive species on Earth is the water hyacinth. These plants are harmful to biodiversity and create negative impacts on society and economy. The Guadiana river (one of the most important ones in Spain) has suffered from this problem since the early 2000s. Several efforts have been made to mitigate it. However, invasive plants, such as the water hyacinth, are still present in seed banks at the bottom of the river and can germinate even more than a decade after. In this article, we propose an automatic methodology, based on remote sensing and deep learning techniques, to monitor the water hyacinth in the Guadiana river. Specifically, a multitemporal analysis was carried out during two years using images collected by ESA's Sentinel-2 satellite, analyzed with a convolutional neural network. We demonstrate that, with our strategy, the river can be monitored every few days, and we are able to automatically detect the water hyacinth. Three experiments have been carried out to predict the presence of water hyacinth from a few scattered training samples, which represent invasive plants in different phenological stages and with different spectral responses.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Burned Area Mapping Using Unitemporal PlanetScope Imagery With a Deep
           Learning Based Approach

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      Authors: Ah Young Cho;Si-eun Park;Duk-jin Kim;Junwoo Kim;Chenglei Li;Juyoung Song;
      Pages: 242 - 253
      Abstract: The risk and damage of wildfires have been increasing due to various reasons including climate change, and the Republic of Korea is no exception to this situation. Burned area mapping is crucial not only to prevent further damage but also to manage burned areas. Burned area mapping using satellite data, however, has been limited by the spatial and temporal resolution of satellite data and classification accuracy. This article presents a new burned area mapping method, by which damaged areas can be mapped using semantic segmentation. For this research, PlanetScope imagery that has high-resolution images with very short revisit time was used, and the proposed method is based on U-Net which requires a unitemporal PlanetScope image. The network was trained using 17 satellite images for 12 forest fires and corresponding label images that were obtained semiautomatically by setting threshold values. Band combination tests were conducted to produce an optimal burned area mapping model. The results demonstrated that the optimal and most stable band combination is red, green, blue, and near infrared of PlanetScope. To improve classification accuracy, Normalized Difference Vegetation Index, dissimilarity extracted from Gray-Level Co-Occurrence Matrix, and Land Cover Maps were used as additional datasets. In addition, topographic normalization was conducted to improve model performance and classification accuracy by reducing shadow effects. The F1 scores and overall accuracies of the final image segmentation models are ranged from 0.883 to 0.939, and from 0.990 to 0.997, respectively. These results highlight the potential of detecting burned areas using the deep learning based approach.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Multispectral Crop Yield Prediction Using 3D-Convolutional Neural Networks
           and Attention Convolutional LSTM Approaches

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      Authors: Seyed Mahdi Mirhoseini Nejad;Dariush Abbasi-Moghadam;Alireza Sharifi;Nizom Farmonov;Khilola Amankulova;Mucsi Lászlź;
      Pages: 254 - 266
      Abstract: In recent years, national economies are highly affected by crop yield predictions. By early prediction, the market price can be predicted, importing, and exporting plan can be provided, social, and economic effects of waste products can be minimized, and a program can be presented for humanitarian food aid. In addition, agricultural fields are constantly growing to generate products required. The use of machine learning (ML) methods in this sector can lead to the efficient production and high-quality agricultural products. Traditional predictive machine models were unable to find nonlinear relationships between data. Recently, there has been a revolution in prediction systems via the advancement of ML, which can be used to achieve highly accurate decision-making networks. Thus far, many strategies have been used to evaluate agricultural products, such as DeepYield, CNN-LSTM, and ConvLSTM. However, preferable prediction accuracy is required. In this study, two architectures have been proposed. The first model includes 2D-CNN, skip connections, and LSTM-Attentions. The second model comprises 3D-CNN, skip connections, and ConvLSTM Attention. The Input data given from MODIS products such as Land-Cover, Surface-Temperature, and MODIS-Land-surface from 2003 to 2018 on the county level over 1800 counties, where soybean is mainly cultivated in the USA. The proposed methods have been compared with the most recent models. Then, the results showed that the second proposed method notably outperformed the other techniques. In case of MAE, the second proposed method, DeepYield, ConvLSTM, 3DCNN, and CNN-LSTM obtained 4.3, 6.003, 6.05, 6.3, and 7.002, respectively.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Evaluation and Improvement of FY-4A/AGRI Sea Surface Temperature
           Data

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      Authors: Quanjun He;Xin Hu;Yanwei Wu;
      Pages: 267 - 277
      Abstract: The advanced geosynchronous radiation imager (AGRI) aboard the Chinese Fengyun-4A (FY-4A) satellite can provide operational hourly sea surface temperature (SST) product. However, the temporal and spatial variation of the errors for this product is still unclear. In this article, FY-4A/AGRI SST is evaluated using the in situ SST from 2019-2021, and a cumulative distribution function matching method is adopted to reduce the errors. Statistical results show that the mean bias and root-mean-square error (RMSE) of FY-4A/AGRI SST are −0.37 °C and 0.98 °C, the median and robust standard deviation (RSD) are −0.30 °C and 0.90 °C. The variations in daily and monthly errors are large and there are no prominent seasonal variations during the period analyzed. There are negative biases exceeding −1.0 °C in low-mid latitude regions and larger positive biases in southern high latitude region. There are dependencies of satellite SST minus in situ SST on satellite zenith angle and on SST itself. After the bias correction, the bias and RMSE are reduced to −0.02 °C and 0.72 °C, and the median and RSD are reduced to 0.00 °C and 0.60 °C. On the time scale, the fluctuation ranges of bias and median are smaller. The difference of satellite SST minus in situ SST can reflect the diurnal variation of SST. The biases are generally within ±0.2 °C in full disk. The error dependencies on satellite zenith angle and SST are also greatly reduced.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • An Automatic and Accurate Method for Marking Ground Control Points in
           Unmanned Aerial Vehicle Photogrammetry

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      Authors: Linghao Kong;Ting Chen;Taibo Kang;Qing Chen;Di Zhang;
      Pages: 278 - 290
      Abstract: Owing to the rapid development of unmanned aerial vehicle (UAV) technology and various photogrammetric software, UAV photogrammetry projects are becoming increasingly automated. However, marking ground control points (GCPs) in current UAV surveys still generally needs to be manually completed, which brings the problem of inefficiency and human error. Based on the characteristics of UAV photogrammetry, a novel type of circular coded target with its identification and decoding algorithm is proposed to realize an automatic and accurate approach for marking GCPs. UAV survey experiments validate the feasibility of the proposed method, which has comparative advantages in efficiency, robustness, and accuracy over traditional targets. Additionally, we conducted experiments to discuss the effects of projection size and viewing angle, number of coded bits, and environmental conditions on the proposed method. The results show that it can achieve robust identification and accurate positioning even under challenging conditions, and a smaller number of coded bits is recommended for better robustness.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • PVT-SAR: An Arbitrarily Oriented SAR Ship Detector With Pyramid Vision
           Transformer

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      Authors: Yue Zhou;Xue Jiang;Guozheng Xu;Xue Yang;Xingzhao Liu;Zhou Li;
      Pages: 291 - 305
      Abstract: The development of deep learning has significantly boosted the development of ship detection in synthetic aperture radar (SAR) images. Most previous works rely on the convolutional neural networks (CNNs), which extract characteristics through local receptive fields and are sensitive to noise. Moreover, these detectors have limited performance in large-scale and complex scenes due to the strong interference of inshore background and the variability of target imaging characteristics. In this article, a novel SAR ship detection framework is proposed, which establishes the pyramid vision transformer (PVT) paradigm for multiscale feature representations in SAR images and, hence, is referred to as PVT-SAR. It breaks the limitation of the CNN receptive field and captures the global dependence through the self-attention mechanism. Since the difficulties of object detection in SAR and natural images are quite different, directly applying the existing transformer structure, such as PVT-small, cannot achieve satisfactory performance for SAR object detection. Compared with the PVT, overlapping patch embedding and mixed transformer encoder modules are incorporated to overcome the problems of densely arranged targets and insufficient data. Then, a multiscale feature fusion module is designed to further improve the detection ability for small targets. Moreover, a normalized Gaussian Wasserstein distance loss is employed to suppress the influence of scattering interference at the ship's boundary. The superiority of the proposed PVT-SAR detector over several state-of-the-art-oriented bounding box detectors has been evaluated in both inshore and offshore scenes on two commonly used SAR ship datasets (i.e., RSSDD and HRSID).
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • SWDet: Anchor-Based Object Detector for Solid Waste Detection in Aerial
           Images

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      Authors: Liming Zhou;Xiaohan Rao;Yahui Li;Xianyu Zuo;Yang Liu;Yinghao Lin;Yong Yang;
      Pages: 306 - 320
      Abstract: As we all know, waste pollution is one of the most serious environmental issues in the world. Efficient detection of solid waste (SW) in aerial images can improve subsequent waste classification and automatic sorting on the ground. However, traditional methods have some problems, such as poor generalization and limited detection performance. This article presents an anchor-based object detector for solid waste in aerial images (SWDet). Specifically, we construct asymmetric deep aggregation (ADA) network with structurally reparameterized asymmetric blocks to extract waste features with inconspicuous appearance. Besides, considering the waste with blurred boundaries caused by the resolution of aerial images, this article constructs efficient attention fusion pyramid network (EAFPN) to obtain contextual information and multiscale geospatial information via attention fusion. And the model can capture the scattering features of irregular shape waste. In addition, we construct the dataset for solid waste aerial detection (SWAD) by collecting aerial images of SW in Henan Province, China, to validate the effectiveness of our method. Experimental results show that SWDet outperforms most of existing methods for SW detection in aerial images.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Remote Sensing Scene Classification Via Multigranularity Alternating
           Feature Mining

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      Authors: Qian Weng;Zhiming Huang;Jiawen Lin;Cairen Jian;Zhengyuan Mao;
      Pages: 318 - 330
      Abstract: Models based on convolutional neural networks (CNNs) have achieved remarkable advances in high-resolution remote sensing (HRRS) images scene classification, but there are still challenges due to the high similarity among different categories and loss of local information. To address this issue, a multigranularity alternating feature mining (MGA-FM) framework is proposed in this article to learn and fuse both global and local information for HRRS scene classification. First, a region confusion mechanism is adopted to guide network's shallow layers to adaptively learn the salient features of distinguishing regions. Second, an alternating comprehensive training strategy is designed to capture and fuse shallow local feature information and deep semantic information to enhance feature representation capabilities. In particular, the MGA-FM framework can be flexibly embedded in various CNN backbone networks as a training mechanism. Extensive experimental results and visualization analysis on three remote sensing scene datasets indicated that the proposed method can achieve competitive classification performance.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Correction of Sea Surface Wind Speed Based on SAR Rainfall Grade
           Classification Using Convolutional Neural Network

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      Authors: Chaogang Guo;Weihua Ai;Xi Zhang;Yanan Guan;Yin Liu;Shensen Hu;Xianbin Zhao;
      Pages: 321 - 328
      Abstract: The technology of retrieving sea surface wind field from spaceborne synthetic aperture radar (SAR) is increasingly mature. However, the retrieval of the sea surface wind field related to the precipitation effect is still facing challenges, especially the strong precipitation related to extreme weather such as tropical cyclone will cause the wind speed retrieval error to exceed 10 m/s. Semantic segmentation and weak supervision methods have been used for SAR rainfall recognition, but rainfall segmentation is not accurate enough to support the correction of wind field retrieval. In this article, we propose to use deep learning to classify the rainfall grades in SAR images, and combine the rainfall correction model to improve the retrieval accuracy of sea surface wind speed. To overcome the challenge of limited training samples, the transfer learning method in fine-tune is adopted. Preliminary results demonstrate the effectiveness of this deep learning methodology. The model classifies rain and no-rain images with an accuracy of 96.2%, and classifies rainfall intensity grades with an accuracy of 86.2%. The rainfall correction model with SAR rainfall grade identified by convolution neural network reduces the root-mean-square error of retrieved wind speed from 3.83 to 1.76 m/s. The combination of SAR rainfall grade recognition and rainfall correction method improves the retrieval accuracy of SAR wind speed, which can further promote the operational application of SAR wind field.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • SAR Target Recognition via Random Sampling Combination in Open-World
           Environments

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      Authors: Xiaojing Geng;Ganggang Dong;Ziheng Xia;Hongwei Liu;
      Pages: 331 - 343
      Abstract: Target recognition in SAR images was widely studied over the years. Most of these works were usually based on the assumption that the targets in the test set belong to a limited set of classes. In the practical scenarios, it is common to encounter various kinds of new targets. It is therefore more meaningful to study target recognition in open-world environments. In these scenes, it is needed to reject the unknown classes while maintain the classification performance on known classes. In the past years, few works were devoted to open set target recognition. Though the detection performance on unknown targets can be improved to a certain extent in the preceding works, most detection schemes are independent of a pretrained feature extractor, leading to potential open space risks. Besides, the model architectures are complicated, resulting in huge computational cost. To solve these problems, a family of new methods for open set target recognition is proposed. Targets indistinguishable from known classes are constructed by random sampling combination strategy. They are further sent into the classifier for feature learning. The original open-world environment is then transformed into a closed-world environment containing the unknown class. Moreover, the special implication of generated unknown targets is highlighted and used to realize unknown detection. Extensive experimental results on the MSTAR benchmark dataset illustrate the effectiveness of the proposed methods.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A Fast Large-Scale Path Planning Method on Lunar DEM Using Distributed
           Tile Pyramid Strategy

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      Authors: Zhonghua Hong;Bin Tu;Xiaohua Tong;Haiyan Pan;Ruyan Zhou;Yun Zhang;Yanling Han;Jing Wang;Shuhu Yang;Zhenling Ma;
      Pages: 344 - 355
      Abstract: In lunar exploration missions, path planning for lunar rovers using digital elevation models (DEMs) is currently a hot topic in academic research. However, research on path planning using large-scale DEMs has rarely been discussed, owing to the low time efficiency of existing algorithms. Therefore, in this article, we propose a fast path-planning method using a distributed tile pyramid strategy and an improved A* algorithm. The proposed method consists of three main steps. First, the tile pyramid is generated for the large lunar DEM and stored in Hadoop distributed file system. Second, a distributed path-planning strategy based on tile pyramid (DPPS-TP) is used to accelerate path-planning tasks on large-scale lunar DEMs using Spark and Hadoop. Finally, an improved A* algorithm was proposed to improve the speed of the path-planning task in each tile. The method was tested using lunar DEM images. Experimental results demonstrate that: in a single-machine serial strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed A* algorithm for open list and closed list with random access feature (OC-RA-A* algorithm) is 3.59 times faster than the traditional A* algorithm in long-distance path planning tasks and compared to the distributed parallel computation strategy using source DEM generated by the Chang'e-2 CCD stereo camera, the proposed DPPS-TP based on tile pyramid DEM is 113.66 times faster in the long-range path planning task.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Hypothetical Cirrus Band Generation for Advanced Himawari Imager Sensor
           Using Data-to-Data Translation With Advanced Meteorological Imager
           Observations

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      Authors: Jeong-Eun Park;Yun-Jeong Choi;Jaehoon Jeong;Sungwook Hong;
      Pages: 356 - 368
      Abstract: Cirrus cloud contributes significantly to earth's radiation budget and the greenhouse effect. The Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite lacks a 1.37 μm band, sensitive to monitoring cirrus clouds. This article proposed a conditional generative adversarial network-based data-to-data translation (D2D) model to generate a hypothetical AHI 1.37 μm band. For training and testing the D2D model, the Geo-Kompsat-2A Advanced Meteorological Imager (AMI) 1.37 μm bands and other highly correlated bands to cirrus from July 24, 2019 to July 31, 2020, were used. The D2D model exhibited a high level of agreement (mean of statistics: correlation coefficient (CC) = 0.9827, bias = 0.0004, and root-mean-square error (RMSE) = 0.0086 in albedo units) between the observed and D2D-generated AMI 1.37 μm bands from validation datasets. The application of the D2D model to the AHI sensor showed that the D2D-generated AHI 1.37 μm band was qualitatively analogous to the observed AMI 1.37 μm band (average of statistics: bias = 0.0026, RMSE = 0.0191 in albedo units, and CC = 0.9158) on the 1st, 15th, and 28th of each month of 2020 in the common observing regions between Korea and Japan. The validation results with the CALIPSO data also showed that the D2D-generated AHI 1.37 μm band performed similarly to the observed AMI 1.37 µm band. Consequently, this article can significantly contribute to cirrus detection and its application to climatology.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Improved Swin Transformer-Based Semantic Segmentation of Postearthquake
           Dense Buildings in Urban Areas Using Remote Sensing Images

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      Authors: Liangyi Cui;Xin Jing;Yu Wang;Yixuan Huan;Yang Xu;Qiangqiang Zhang;
      Pages: 369 - 385
      Abstract: Timely acquiring the earthquake-induced damage of buildings is crucial for emergency assessment and post-disaster rescue. Optical remote sensing is a typical method for obtaining seismic data due to its wide coverage and fast response speed. Convolutional neural networks (CNNs) are widely applied for remote sensing image recognition. However, insufficient extraction and expression ability of global correlations between local image patches limit the performance of dense building segmentation. This paper proposes an improved Swin Transformer to segment dense urban buildings from remote sensing images with complex backgrounds. The original Swin Transformer is used as a backbone of the encoder, and a convolutional block attention module is employed in the linear embedding and patch merging stages to focus on significant features. Hierarchical feature maps are then fused to strengthen the feature extraction process and fed into the UPerNet (as the decoder) to obtain the final segmentation map. Collapsed and non-collapsed buildings are labeled from remote sensing images of the Yushu and Beichuan earthquakes. Data augmentations of horizontal and vertical flipping, brightness adjustment, uniform fogging, and non-uniform fogging are performed to simulate actual situations. The effectiveness and superiority of the proposed method over the original Swin Transformer and several mature CNN-based segmentation models are validated by ablation experiments and comparative studies. The results show that the mean intersection-over-union of the improved Swin Transformer reaches 88.53%, achieving an improvement of 1.3% compared to the original model. The stability, robustness, and generalization ability of dense building recognition under complex weather disturbances are also validated.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Regional Characteristics and Impact Factors of Change in Terrestrial Water
           Storage in Northwestern China From 2002 to 2020

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      Authors: Jianguo Yin;Jiahua Wei;Qiong Li;Olusola O. Ayantobo;
      Pages: 386 - 398
      Abstract: This article characterized the linear trends and interannual signals of terrestrial water storage (TWS) and meteorological variables including precipitation (P) and evapotranspiration (ET) over the arid Northwestern China (NWC). The relative impaction of P, ET, and human water utilization (HU) on TWS variation among the 10 watersheds of NWC (i.e., 5 watersheds in Xinjiang, 3 watersheds in Hexi Corridor, and 2 watersheds in Qinghai) were then investigated. The result indicated that groundwater storage (GWS) was the main contributor to the TWS variation and matched well with TWS in spatial features or watershed-scale variations. The entire NWC presented growth trends for P (0.05 cm/year) and ET (0.22 cm/year) and decline trends for TWS (−0.19 cm/year) and GWS (−0.20 cm/year). The watersheds in Qinghai province where mainly affected by natural factors showed the increasing TWS/GWS trend. The watersheds in Xinjiang and Hexi Corridor, which had strong impact from human activities generally showed the declining TWS/GWS trends, but Xinjiang showed more intensive declining trend than Hexi Corridor. The analysis of HU indicated that water sustainable management and water-saving technologies had effectively kept down the tendency of TWS/GWS declining in the watersheds in Hexi Corridor, however, they were not sufficient to address the water shortage caused by farmland expansion, slight P growth, and high ET growth in Xinjiang. Groundwater use, as the main source to compensate for the increase in HU (especially agricultural water use), exacerbated TWS/GWS loss in Xinjiang. This article provides valuable information for the water management over the arid NWC.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A New Method for Estimating Signal-to-Noise Ratio in UAV Hyperspectral
           Images Based on Pure Pixel Extraction

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      Authors: Wenzhong Tian;Qingzhan Zhao;Za Kan;Xuefeng Long;Hanqing Liu;Juntao Cheng;
      Pages: 399 - 408
      Abstract: Signal-to-noise ratio (SNR) is an important radiation characteristic parameter for remote sensing image quality assessment as well as a key performance indicator for remote sensing sensors. At present, the SNR estimation methods based on regular segmentation or continuous segmentation are generally used to obtain image SNR. However, the land cover type has a great influence on the results of the SNR estimation method using regular segmentation, especially the high spectral resolution and high spatial resolution remote sensing images obtained by the low-altitude UAV hyperspectral sensor. In addition, some land cover types are difficult to achieve continuous segmentation. In view of this limitation of the existing SNR estimation methods, a new unsupervised method for estimating SNR in UAV hyperspectral images has been developed in this article, called pure pixel extraction and spectral decorrelation. By directly extracting pure pixels in the spatial dimension and combining the correlation of the spectral dimension to obtain the SNR of the hyperspectral image, this new method replaces the conventional method of improving the segmentation algorithm to improve the accuracy of SNR estimation. Additionally, the box counting method is introduced to determine the image SNR aggregation interval. The results showed that the proposed method had higher accuracy and smaller errors than the other SNR estimation methods. Besides, this method had stronger robustness, it can be used for both radiance and reflectance (atmospherically corrected) UAV hyperspectral images.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A Two-Step Ensemble-Based Genetic Algorithm for Land Cover Classification

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      Authors: Yang Cao;Wei Feng;Yinghui Quan;Wenxing Bao;Gabriel Dauphin;Yijia Song;Aifeng Ren;Mengdao Xing;
      Pages: 409 - 418
      Abstract: Accurate land use and land cover (LULC) maps are effective tools to help achieve sound urban planning and precision agriculture. As an intelligent optimization technology, genetic algorithm (GA) has been successfully applied to various image classification tasks in recent years. However, simple GA faces challenges, such as complex calculation, poor noise immunity, and slow convergence. This research proposes a two-step ensemble protocol for LULC classification using a grayscale-spatial-based GA model. The first ensemble framework uses fuzzy c-means to classify pixels into those that are difficult to cluster and those that are easy to cluster, which aids in reducing the search space for evolutionary computation. The second ensemble framework uses neighborhood windows as heuristic information to adaptively modify the objective function and mutation probability of the GA, which brings valuable benefits to the discrimination and decision of GA. In this study, three research areas in Dangyang, China, are utilized to validate the effectiveness of the proposed method. The experiments show that the proposed method can effectively maintain the image details, restrain noise, and achieve rapid algorithm convergence. Compared with the reference methods, the best overall accuracy obtained by the proposed algorithm is 88.72%.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Spatial–Spectral Split Attention Residual Network for Hyperspectral
           Image Classification

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      Authors: Zhenqiu Shu;Zigao Liu;Jun Zhou;Songze Tang;Zhengtao Yu;Xiao-Jun Wu;
      Pages: 419 - 430
      Abstract: In the past few years, many convolutional neural networks (CNNs) have been applied to hyperspectral image (HSI) classification. However, many of them have the following drawbacks: they do not fully consider the abundant band spectral information and insufficiently extract the spatial information of HSI; all bands and neighboring pixels are treated equally, so CNNs may learn features from redundant or useless bands/pixels; and a significant amount of hidden semantic information is lost when a single-scale convolution kernel is used in CNNs. To alleviate these problems, we propose a spatial–spectral split attention residual networks (S$^{3}$ARN) for HSI classification. In S$^{3}$ARN, a split attention strategy is used to fuse the features extracted from multireceptive fields, in which both spectral and spatial split attention modules are composed of bottleneck residual blocks. Thanks to the bottleneck structure, the proposed method can effectively prevent overfitting, speeds up the model training, and reduces the network parameters. Moreover, the spectral and spatial attention residual branches aim to generate the attention masks, which can simultaneously emphasize useful bands and neighbor pixels and suppress useless ones. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed model for HSI classification.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Cross Field-Based Segmentation and Learning-Based Vectorization for
           Rectangular Windows

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      Authors: Xiangyu Zhuo;Jiaojiao Tian;Friedrich Fraundorfer;
      Pages: 431 - 448
      Abstract: Detection and vectorization of windows from building façades are important for building energy modeling, civil engineering, and architecture design. However, current applications still face the challenges of low accuracy and lack of automation. In this article we propose a new two-steps workflow for window segmentation and vectorization from façade images. First, we propose a cross field learning-based neural network architecture, which is augmented by a grid-based self-attention module for window segmentation from rectified façade images, resulting in pixel-wise window blobs. Second, we propose a regression neural network augmented by squeeze-and-excitation (SE) attention blocks for window vectorization. The network takes the segmentation results together with the original façade image as input, and directly outputs the position of window corners, resulting in vectorized window objects with improved accuracy. In order to validate the effectiveness of our method, experiments are carried out on four public façades image datasets, with results usually yielding a higher accuracy for the final window prediction in comparison to baseline methods on four datasets in terms of intersection over union score, F1 score, and pixel accuracy.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Multicascaded Feature Fusion-Based Deep Learning Network for Local Climate
           Zone Classification Based on the So2Sat LCZ42 Benchmark Dataset

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      Authors: Weizhen Ji;Yunhao Chen;Kangning Li;Xiujuan Dai;
      Pages: 449 - 467
      Abstract: A detailed investigation of the microclimate is beneficial for optimizing the urban inner/spatial pattern to enhance thermal comfort or even reduce heatwave disasters, whereas accurately classifying local climate zones (LCZs) severely restricts analysis of thermal characterization. Generally, deep learning-based approaches are effective for adaptive LCZ mapping, yet they often have poor accuracy because inadequate cascade feature extraction patterns may not adapt to the fuzzy LCZ boundaries produced by intricate urban textures, especially when using large-scale datasets. To address these issues, we propose a novel CNN model in which we design a strategy that incorporates a triple feature fusion pattern to enhance LCZ recognition based on the So2sat LCZ 42 large-scale annotated dataset. The approach connects multilayer cascaded information to participate in category judgment, which avoids the loss of effective feature information via continuous cascade transformation as much as possible. The results show that the overall accuracy and kappa coefficient of the proposed model reach 0.70 and 0.68, respectively, manifesting an improvement of approximately 4.47% and 6.25% over advanced LCZ classification approaches. In particular, the accuracy of the proposed approach improves even further after the fusion structure or layer depth is partially removed or reduced, respectively. Finally, we discuss several items, including the effectiveness of different parameters and cascaded feature fusion patterns, the superiority of multilayer cascade feature fusion, the mapping impact of seasons and cloud cover, and even some other issues in LCZ mapping. This article will facilitate improvements in the research precision of urban thermal environments.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Determination of the Spatial Extent of the Engine Exhaust-Disturbed Region
           of the Chang'E-4 Landing Site Using LROC NAC Images

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      Authors: Yaqiong Wang;Huan Xie;Chao Wang;Xiaohua Tong;Sicong Liu;Xiong Xu;
      Pages: 468 - 481
      Abstract: The regolith of the Chang'E-4 landing site was disturbed by its engine exhaust. To explore the interaction between the engine exhaust and the regolith, it was necessary to identify the exhaust-disturbed region. This article focuses on determining the extent of the disturbed region by using lunar reconnaissance orbiter camera narrow angle camera (LROC NAC) images. For this purpose, the tools of temporal-ratio images, phase-ratio images, reflectance profiles, and reflectance isoline graphs are employed. The reflectance profiles and isoline graphs derived from the temporal-ratio images reveal the reflectance changes before and after landing. Compared with the reflectance profiles, isoline graphs further include the spatial information of isolines, thus more robust to noise. Based on the magnitudes of changed reflectance around the lander, the engine exhaust-disturbed region was further divided into the focus disturbed region (FDR) and the diffuse disturbed region (DDR). The final estimated spatial extent along the north–south and east–west directions of the FDR were ∼9.6 and ∼10.8 m, and those of the DDR were ∼75 and ∼80 m. As compared with the estimated spatial extent of the Chang'E-3 landing site, the DDR of the Chang'E-4 landing site was larger, but the FDR was smaller. We attributed this to geological and topography factors. The reflectance changes between the FDR and the undisturbed region increased by ∼10±1%. This indicates similar processes causing the variations in the regolith properties, likely including the smoothing of the surface from microscopic to macroscopic by destroying fine-grained regolith components, or changing of the surface maturity.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • High-Resolution Semantically Consistent Image-to-Image Translation

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      Authors: Mikhail Sokolov;Christopher Henry;Joni Storie;Christopher Storie;Victor Alhassan;Mathieu Turgeon-Pelchat;
      Pages: 482 - 492
      Abstract: Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation (DA) problem to narrow the discrepancy between satellite image datasets. As a result, image segmentation models that are then trained, could better generalize and use an existing set of labels instead of requiring new ones. This work proposes an unsupervised DA model that preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. This article's major contribution is proposing the improved architecture of the SemI2I model, which significantly boosts the proposed model's performance and makes it competitive with the state-of-the-art CyCADA model. A second contribution is testing the CyCADA model on the remote sensing multiband datasets, such as WorldView-2 and SPOT-6. The proposed model preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. Thus, the semantic segmentation model, trained on the adapted images, shows substantial performance gain compared to the SemI2I model and reaches similar results as the state-of-the-art CyCADA model. The future development of the proposed method could include ecological domain transfer, a priori evaluation of dataset quality in terms of data distribution, or exploration of the inner architecture of the DA model.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Development of Simulation Models Supporting Next-Generation Airborne
           Weather Radar for High Ice Water Content Monitoring

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      Authors: Yunish Shrestha;Yan Zhang;Greg M. McFarquhar;William Blake;Mariusz Starzec;Steven D. Harrah;
      Pages: 493 - 507
      Abstract: In this article, a method of extending airborne weather radar modeling to incorporate high-ice-water-content (HIWC) conditions has been developed. A novel aspect is incorporating flight test measurement data, including forward-looking radar measurements and in situ microphysics probes data, into the model and part of the evaluations of modeling. The simulation models assume a dual-polarized, airborne forward-looking radar, while for single-polarized operations, the index-of-dispersion is included as a helpful indicator for HIWC detection. The radar system simulation models are useful for design evaluations for the next generation of airborne aviation hazard monitoring and incorporate HIWC hazard detection algorithms. Example applications of the simulator, such as hazard detection based on the simulated HIWC flight encounter scenarios based on specific numeric weather prediction (NWP) model outputs, are discussed.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • GNSS-Based Passive Inverse SAR Imaging

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      Authors: Pengbo Wang;Xinkai Zhou;Yue Fang;Hongcheng Zeng;Jie Chen;
      Pages: 508 - 521
      Abstract: The utilization of global navigation satellite system (GNSS) signals for remote sensing has been a hot topic recently. In this article, the feasibility of the GNSS-based passive inverse synthetic aperture radar (P-ISAR) is analyzed. GNSS-based P-ISAR can generate the two-dimensional image of a moving target, providing an estimation of the target size, which is very important information in target recognition. An effective GNSS-based P-ISAR moving target imaging algorithm is proposed. First, a precise direct path interference (DPI) suppression method is derived to eliminate the DPI power in the detection channel. Then, the P-ISAR signal processing method is established. Due to the large synthetic aperture time, the Doppler profile of the ISAR image will defocus if directly performing the Fourier transform. As a solution, a parametric autofocusing and cross-range scaling algorithm is specially tailored for the GNSS-based P-ISAR. The proposed algorithm cannot only focus and scale the ISAR image, but also provide an estimation of the cross-range velocity of the target. Simulation with an airplane target is designed to test the signal processing method. Finally, an experiment is conducted with a civil airplane as the target and GPS satellites as the illumination source. Focused ISAR image is successfully acquired. The estimated length and velocity of the target are approximately consistent with ground truth, which are obtained by the flight record. The potential of the GNSS-based P-ISAR on multistatic operations is also illustrated by the fusion of the ISAR images obtained using different satellites as illumination sources.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Spectral–Spatial Generative Adversarial Network for Super-Resolution
           Land Cover Mapping With Multispectral Remotely Sensed Imagery

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      Authors: Cheng Shang;Shan Jiang;Feng Ling;Xiaodong Li;Yadong Zhou;Yun Du;
      Pages: 522 - 537
      Abstract: Super-resolution mapping (SRM) can effectively predict the spatial distribution of land cover classes within mixed pixels at a higher spatial resolution than the original remotely sensed imagery. The uncertainty of land cover fraction errors within mixed pixels is one of the most important factors affecting SRM accuracy. Studies have shown that SRM methods using deep learning techniques have significantly improved land cover mapping accuracy but have not coped well with spectral–spatial errors. This study proposes an end-to-end SRM model using a spectral–spatial generative adversarial network (SGS) with the direct input of multispectral remotely sensed imagery, which deals with spectral–spatial error. The proposed SGS comprises the following three parts: first, cube-based convolution for spectral unmixing is adopted to generate land cover fraction images. Second, a residual-in-residual dense block fully and jointly considers spectral and spatial information and reduces spectral errors. Third, a relativistic average GAN is designed as a backbone to further improve the super-resolution performance and reduce spectral–spatial errors. SGS was tested in one synthetic and two realistic experiments with multi/hyperspectral remotely sensed imagery as the input, comparing the results with those of hard classification and several classic SRM methods. The results showed that SGS performed well at reducing land cover fraction errors, reconstructing spatial details, removing unpleasant and unrealistic land cover artifacts, and eliminating false recognition.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Global Unsupervised Assessment of Multifrequency Vegetation Optical Depth
           Sensitivity to Vegetation Cover

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      Authors: Claudia Olivares-Cabello;David Chaparro;Mercè Vall-llossera;Adriano Camps;Carlos López-Martínez;
      Pages: 538 - 552
      Abstract: Vegetation optical depth (VOD) has contributed to monitor vegetation dynamics and carbon stocks at different microwave frequencies. Nevertheless, there is a need to determine which are the appropriate frequencies to monitor different vegetation types. Also, as only a few VOD-related studies use multifrequency approaches, it is needed to evaluate their applicability. Here, we analyze the sensitivity of VOD at three frequencies (L-, C-, and X-bands) to different vegetation covers by applying a global-scale unsupervised classification of VOD. A combination of these frequencies (LCX-VOD) is also studied. Two land cover datasets are used as benchmarks and, conceptually, serve as proxies of vegetation density. Results confirm that L-VOD is appropriate for monitoring the densest canopies but, in contrast, there is a higher sensitivity of X-, C-, and LCX-VOD to the vegetation cover in savannahs, shrublands, and grasslands. In particular, the multifrequency combination is the most suited to sense vegetation in savannahs. Also, our study shows a vegetation–frequency relationship that is consistent with theory: the same canopies (e.g., savannahs and some boreal forests) are classified as lighter ones at L-band due to its higher penetration (e.g., as shrublands), but labeled as denser ones at C- and X-bands due their saturation (e.g., boreal forests are labeled as tropical forests). This study complements quantitative approaches investigating the link between VOD and vegetation, extends them to different frequencies, and provides hints on which frequencies are suitable for vegetation monitoring depending on the land cover. Conclusions are informative for upcoming multifrequency missions, such as the Copernicus Multifrequency Image Radiometer.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Novel Air2water Model Variant for Lake Surface Temperature Modeling With
           Detailed Analysis of Calibration Methods

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      Authors: Adam P. Piotrowski;Jaroslaw J. Napiorkowski;Senlin Zhu;
      Pages: 553 - 569
      Abstract: The air2water model is a simple and efficient tool for modeling surface water temperature in lakes based solely on the air temperature. In this article, we propose to modify the air2water model in such a way that different parameters would be associated with lake stratification of cold waters than with lake stratification of warm waters. The situation of a mix of both cold water and warm water is also considered. The model is tested on 22 lowland Polish lakes against two classical air2water variants. As the new air2water model variant is slightly more complicated than the basic versions, we focus on the importance of the choice of the calibration method. Each variant of the air2water model is calibrated with eight different optimization methods, which are also compared on various benchmark problems. We show that the proposed variant is superior to the classical air2water models on about 90% of tested lakes, but only if the calibration approach is properly selected, which confirms the importance of the links between the model and appropriate optimization procedures. The proposed air2water variant performs well on various lowland lakes, with exception of large but shallow ones, probably due to the weak stratification of the shallow lakes.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • &rft.title=Selected+Topics+in+Applied+Earth+Observations+and+Remote+Sensing,+IEEE+Journal+of&rft.issn=1939-1404&rft.date=2023&rft.volume=16&rft.spage=570&rft.epage=580&rft.aulast=Li;&rft.aufirst=Tong&rft.au=Tong+Xiao;Yiliang+Wan;Jianjun+Chen;Wenzhong+Shi;Jianxin+Qin;Deping+Li;">Multiresolution-Based Rough Fuzzy Possibilistic -Means Clustering Method
           for Land Cover Change Detection

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      Authors: Tong Xiao;Yiliang Wan;Jianjun Chen;Wenzhong Shi;Jianxin Qin;Deping Li;
      Pages: 570 - 580
      Abstract: Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic $c$-means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. First, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Second, objects at the coarsest scale are classified into changed, unchanged, and uncertain categories by the proposed MRFPCM. Third, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged, and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three datasets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A Novel Tensor-Based Hyperspectral Image Restoration Method With Low-Rank
           Modeling in Gradient Domains

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      Authors: Pengfei Liu;Lanlan Liu;Liang Xiao;
      Pages: 581 - 597
      Abstract: The hyperspectral image (HSI) is easily contaminated by various kinds of mixed noise (such as Gaussian noise, impulse noise, stripes, and deadlines) during the process of data acquisition and conversion, which significantly affect the quality and applications of HSI. As an important and effective scheme for the quality improvement of HSI, the HSI restoration problem aims to recover a clean HSI from the noisy HSI with mixed noise. Thus, based on the tensor modeling of HSI, we propose a novel tensor-based HSI restoration model with low-rank modeling in gradient domains in a unified tensor representation framework in this article. First, for the spectral low-rank modeling of HSI in spectral gradient domain, we particularly exploit the low-rank property of spectral gradient, and propose the spectral gradient-based weighted nuclear norm low-rank prior term. Second, for the spatial-mode low-rank modeling of HSI in spatial gradient domain, we particularly exploit the low-rank property of spatial gradient tensors via the discrete Fourier transform, and propose the spatial gradient-based tensor nuclear norm low-rank prior term. Then, we use the alternative direction method of multipliers to solve the proposed model. Finally, the restoration results on both the simulated and real HSI datasets demonstrate that the proposed method is superior to many state-of-the-art methods in the aspects of visual and quantitative comparisons.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Tomographic Imaging for Orbital Radar Sounding of Earth's Ice
           Sheets

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      Authors: Min Liu;Peng Xiao;Lu Liu;Xiaohong Sui;Chunzhu Yuan;
      Pages: 598 - 608
      Abstract: BingSat-Tomographic Observation of Polar Ice Sheets (BingSat-TOPIS) is a spaceborne multistatic radar sounding system that can achieve high resolution and stereoscopic observation. It is designed to penetrate ice sheets and acquire tomographic image. The satellite group fly over the Polar Regions, which can form about 6.56-km cross-track baseline. This cross-track baseline is formed by one master satellite and 40 slave CubeSats. In this article, we propose a tomographic imaging algorithm for orbital radar sounding of the Earth's ice sheets. First, we give the method to calculate the two-way slant range in air and ice. The phase errors of the method are smaller than π/4 rad. Second, we express the radar data cube for a point target in ice sheets. The radar data cube is made of 40 bistatic synthetic aperture radar echo signals. At last, the echoes are simulated for the TOPIS system, and a matched filter is used for pulse compression in range. Then, the back projection algorithm is used in track and cross-track imaging. The experimental results demonstrate that our tomographic imaging algorithm is effective and reliable.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • High-Precision ZTD Model of Altitude-Related Correction

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      Authors: Qingzhi Zhao;Jing Su;Chaoqian Xu;Yibin Yao;Xiaoya Zhang;Jifeng Wu;
      Pages: 609 - 621
      Abstract: Zenith tropospheric delay (ZTD) is one of the main error sources in space geodesy. The existing regional or global models, such as Global Pressure and Temperature 3 (GPT3), Global Tropospheric model, Global Hopfield, and Shanghai Astronomical observatory tropospheric delay model models, have good performance. However, the precision of these models is relatively low in regions with a large height difference, which becomes the focus of this article. A high-precision ZTD model considering the height effect on tropospheric delay is proposed, and China is selected as study area due to its large height difference, which is called the high-precision ZTD model for China (CHZ). The initial ZTD value is calculated on the basis of the GPT3 model, and the periodic terms of ZTD residual between the global navigation satellite system (GNSS) and GPT3 model, such as annual, semiannual, and seasonal periods, are determined by the Lomb–Scargle periodogram method in different subareas of China. The relationship between the ZTD periodic residual term and the height of the GNSS station is further analyzed at different seasons, and linear ZTD periodic residual models are obtained. A total of 164 GNSS stations derived from the Crustal Movement Observation Network of China and 87 radiosonde stations are selected to validate the proposed CHZ model, and hourly ZTD data derived from GNSS are used to establish the CHZ model. Statistical result shows that the averaged root mean square and Bias of the CHZ model are 21.12 and −2.51 mm, respectively, in the whole of China. In addition, the application of CHZ model in precision point positioning (PPP) show that the convergence time is improved by 34%, 15%, and 35%, respectively, in N, E, and U components when compared to GPT3-based PPP.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Efficient Global Color, Luminance, and Contrast Consistency Optimization
           for Multiple Remote Sensing Images

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      Authors: Zhonghua Hong;Changyou Xu;Xiaohua Tong;Shijie Liu;Ruyan Zhou;Haiyan Pan;Yun Zhang;Yanling Han;Jing Wang;Shuhu Yang;
      Pages: 622 - 637
      Abstract: Light and color uniformity is essential for the production of high-quality remote-sensing image mosaics. Existing color correction methods mainly use flexible models to express the color differences between multiple images and impose specific constraints (e.g., image gradient or contrast constraints) to preserve image texture information as much as possible. Due to these constraints, it is usually difficult to correct for the differences in texture between images during image processing. We propose a method that can optimize the luminance, contrast, and color difference of remote-sensing images. In the YCbCr color space, this method processes the chrominance and luminance channels of the image. This is conducive to reducing the influence of the different channels. In the luminance channel, the block-based Wallis transform method is used to optimize the luminance and contrast of the image. In the chromaticity channel, to optimize the color differences, a spline curve is used as a model; the color differences are formulated as a cost function and solved using convex quadratic programming. Moreover, considering the efficiency of our method, we use a graphics processing unit to make the algorithm parallel. The proposed method has been tested on several challenging datasets that cover different topographic regions. In terms of visuals and quality indicators, it shows better results than state-of-the-art approaches.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A Multiresolution Details Enhanced Attentive Dual-UNet for Hyperspectral
           and Multispectral Image Fusion

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      Authors: Jian Fang;Jingxiang Yang;Abdolraheem Khader;Liang Xiao;
      Pages: 638 - 655
      Abstract: The fusion-based super-resolution of hyperspectral images (HSIs) draws more and more attention in order to surpass the hardware constraints intrinsic to hyperspectral imaging systems in terms of spatial resolution. Low-resolution (LR)-HSI is combined with a high-resolution multispectral image (HR-MSI) to achieve HR-HSI. In this article, we propose multiresolution details enhanced attentive dual-UNet to improve the spatial resolution of HSI. The entire network contains two branches. The first branch is the wavelet detail extraction module, which performs discrete wavelet transform on MSI to extract spatial detail features and then passes through the encoding–decoding. Its main purpose is to extract the spatial features of MSI at different scales. The latter branch is the spatio-spectral fusion module, which aims to inject the detail features of the wavelet detail extraction network into the HSI to reconstruct the HSI better. Moreover, this network uses an asymmetric feature selective attention model to focus on important features at different scales. Extensive experimental results on both simulated and real data show that the proposed network architecture achieves the best performance compared with several leading HSI super-resolution methods in terms of qualitative and quantitative aspects.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • MVCNN: A Deep Learning-Based Ocean–Land Waveform Classification Network
           for Single-Wavelength LiDAR Bathymetry

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      Authors: Gang Liang;Xinglei Zhao;Jianhu Zhao;Fengnian Zhou;
      Pages: 656 - 674
      Abstract: Ocean–land waveform classification (OLWC) is crucial in airborne LiDAR bathymetry (ALB) data processing and can be used for ocean–land discrimination and waterline extraction. However, the accuracy of OLWC for single-wavelength ALB systems is low given the nature of the green laser waveform in complex environments. Thus, in this article, a deep learning-based OLWC method called the multichannel voting convolutional neural network (MVCNN) is proposed based on the comprehensive utilization of multichannel green laser waveforms. First, multiple green laser waveforms collected in deep and shallow channels are input into a multichannel input module. Second, a one-dimensional (1-D) convolutional neural network (CNN) structure is proposed to handle each green channel waveform. Finally, a multichannel voting module is introduced to perform majority voting on the predicted categories derived by each 1-D CNN model and output the final waveform category (i.e., ocean or land waveforms). The proposed MVCNN is evaluated using the raw green laser waveforms collected by Optech coastal zone mapping and imaging LiDAR (CZMIL). Results show that the overall accuracy, kappa coefficient, and standard deviation of the overall accuracy for the OLWC utilizing green laser waveforms based on MVCNN can reach 99.41%, 0.9800, and 0.03%, respectively. Results further show that the classification accuracy of the MVCNN is improved gradually with the increase in the number of laser channels. The multichannel voting module can select the correct waveform category from the deep and shallow channels. The proposed MVCNN is highly accurate and robust, and it is slightly affected by aquaculture rafts and the merging effect of green laser waveform in very shallow waters. Thus, the use of MVCNN in OLWC for single-wavelength ALB systems is recommended. In addition, this article explores the relationships between green deep and shall-w channel waveforms based on the analysis of CZMIL waveform data.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • LRAD-Net: An Improved Lightweight Network for Building Extraction From
           Remote Sensing Images

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      Authors: Jiabin Liu;Huaigang Huang;Hanxiao Sun;Zhifeng Wu;Renbo Luo;
      Pages: 675 - 687
      Abstract: The building extraction method of remote sensing images that uses deep learning algorithms can solve the problems of low efficiency and poor effect of traditional methods during feature extraction. Although some semantic segmentation networks proposed recently can achieve good segmentation performance in extracting buildings, their huge parameters and large amount of calculation lead to great obstacles in practical application. Therefore, we propose a lightweight network (named LRAD-Net) for building extraction from remote sensing images. LRAD-Net can be divided into two stages: encoding and decoding. In the encoding stage, the lightweight RegNet network with 600 million flop (600 MF) is finally selected as our feature extraction backbone net though lots of experimental comparisons. Then, a multiscale depthwise separable atrous spatial pyramid pooling structure is proposed to extract more comprehensive and important details of buildings. In the decoding stage, the squeeze-and-excitation attention mechanism is applied innovatively to redistribute the channel weights before fusing feature maps with low-level details and high-level semantics, thus can enrich the local and global information of the buildings. What's more, a lightweight residual block with polarized self-attention is proposed, it can incorporate features extracted from the space of maps and different channels with a small number of parameters, and improve the accuracy of recovering building boundary. In order to verify the effectiveness and robustness of proposed LRAD-Net, we conduct experiments on a self-annotated UAV dataset with higher resolution and three public datasets (the WHU aerial image dataset, the WHU satellite image dataset and the Inria aerial image dataset). Compared with several representative networks, LRAD-Net can extract more details of building, and has smaller number of parameters, faster computing speed, stronger generalization ability, which can improve the trai-ing speed of the network without affecting the building extraction effect and accuracy.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Hypergraph-Enhanced Textual-Visual Matching Network for Cross-Modal Remote
           Sensing Image Retrieval via Dynamic Hypergraph Learning

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      Authors: Fanglong Yao;Xian Sun;Nayu Liu;Changyuan Tian;Liangyu Xu;Leiyi Hu;Chibiao Ding;
      Pages: 688 - 701
      Abstract: Cross-modal remote sensing (RS) image retrieval aims to retrieve RS images using other modalities (e.g., text) and vice versa. The relationship between objects in the RS image is complex, i.e., the distribution of multiple types of objects is uneven, which makes the matching with query text inaccurate, and then restricts the performance of remote sensing image retrieval. Previous methods generally focus on the feature matching between RS image and text and rarely model the relationships between features of RS image. Hypergraph (hyperedge connecting multiple vertices) is an extended structure of a regular graph and has attracted extensive attention for its superiority in representing high-order relationships. Inspired by the advantages of the hypergraph, in this work, a hypergraph-enhanced textual-visual matching network (HyperMatch) is proposed to circumvent the inaccurate matching between the RS image and query text. Specifically, a multiscale RS image hypergraph network is designed to model the complex relationships between features of the RS image for forming the valuable and redundant features into different hyperedges. In addition, a hypergraph construction and update method for an RS image is designed. For constructing a hypergraph, the features of an RS image running as vertices and cosine similarity is the metric to measure the correlation between them. Vertex and hyperedge attention mechanisms are introduced for the dynamic update of a hypergraph to realize the alternating update of vertices and hyperedges. Quantitative and qualitative experiments on the RSICD and RSITMD datasets verify the effectiveness of the proposed method in cross-modal remote sensing image retrieval.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Compression Supports Spatial Deep Learning

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      Authors: Gabriel Dax;Srilakshmi Nagarajan;Hao Li;Martin Werner;
      Pages: 702 - 713
      Abstract: In the last decades, the domain of spatial computing became more and more data driven, especially when using remote sensing-based images. Furthermore, the satellites provide huge amounts of images, so the number of available datasets is increasing. This leads to the need for large storage requirements and high computational costs when estimating the label scene classification problem using deep learning. This consumes and blocks important hardware recourses, energy, and time. In this article, the use of aggressive compression algorithms will be discussed to cut the wasted transmission and resources for selected land cover classification problems. To compare the different compression methods and the classification performance, the satellite image patches are compressed by two methods. The first method is the image quantization of the data to reduce the bit depth. Second is the lossy and lossless compression of images with the use of image file formats, such as JPEG and TIFF. The performance of the classification is evaluated with the use of convolutional neural networks (CNNs) like VGG16. The experiments indicated that not all remote sensing image classification problems improve their performance when taking the full available information into account. Moreover, compression can set the focus on specific image features, leading to fewer storage needs and a reduction in computing time with comparably small costs in terms of quality and accuracy. All in all, quantization and embedding into file formats do support CNNs to estimate the labels of images, by strengthening the features.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • DARN: Distance Attention Residual Network for Lightweight Remote-Sensing
           Image Superresolution

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      Authors: Qingjian Wang;Sen Wang;Mingfang Chen;Yang Zhu;
      Pages: 714 - 724
      Abstract: The application of single-image superresolution (SISR) in remote sensing is of great significance. Although the state-of-the-art convolution neural network (CNN)-based SISR methods have achieved excellent results, the large model and slow speed make it difficult to deploy in real remote sensing tasks. In this article, we propose a compact and efficient distance attention residual network (DARN) to achieve a better compromise between model accuracy and complexity. The distance attention residual connection block (DARCB), the core component of the DARN, uses multistage feature aggregation to learn more accurate feature representations. The main branch of the DARCB adopts a shallow residual block (SRB) to flexibly learn residual information to ensure the robustness of the model. We also propose a distance attention block (DAB) as a bridge between the main branch and the branch of the DARCB; the DAB can effectively alleviate the loss of detail features in the deep CNN extraction process. Experimental results on two remote sensing and five super-resolution benchmark datasets demonstrate that the DARN achieves a better compromise than existing methods in terms of performance and model complexity. In addition, the DARN achieves the optimal solution compared with the state-of-the-art lightweight remote sensing SISR method in terms of parameter amount, computation amount, and inference speed. Our code will be available at https://github.com/candygogogogo/DARN.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Hyperspectral Compressive Image Reconstruction With Deep Tucker
           Decomposition and Spatial–Spectral Learning Network

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      Authors: Hao Xiang;Baozhu Li;Le Sun;Yuhui Zheng;Zebin Wu;Jianwei Zhang;Byeungwoo Jeon;
      Pages: 725 - 737
      Abstract: Hyperspectral compressive imaging has taken advantage of compressive sensing theory to capture spectral information of the dynamic world in recent decades of years, where an optical encoder is employed to compress high dimensional signals into a single 2-D measurement. The core issue is how to reconstruct the underlying hyperspectral image (HSI), although deep neural network methods have achieved much success in compressed sensing image reconstruction in recent years, they still have some unsolved issues, such as tradeoffs between performance and efficiency, and accurate exploitation of cubic structure information. In this article, we propose a deep Tucker decomposition and spatial–spectral learning network (DS-net) to learn the tensor low-lank structure features and spatial–spectral correlation of HSI for reconstruction quality promotion. Inspired by tensor decomposition, we first construct a deep Tucker decomposition module to learn the principal components from different modes of the image features. Then, we cascade a series of decomposition modules to learn multihierarchical features. Furthermore, to jointly capture the spatial–spectral correlation of HSI, we propose a spatial–spectral correlation learning module in a U-net structure for more robust reconstruction performance. Finally, experimental results on both synthetic and real datasets demonstrate the superiority of the proposed method compared to several state-of-the-art methods in quantitative assessment and visual effects.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Vision Transformer With Contrastive Learning for Remote Sensing Image
           Scene Classification

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      Authors: Meiqiao Bi;Minghua Wang;Zhi Li;Danfeng Hong;
      Pages: 738 - 749
      Abstract: Remote sensing images (RSIs) are characterized by complex spatial layouts and ground object structures. ViT can be a good choice for scene classification owing to the ability to capture long-range interactive information between patches of input images. However, due to the lack of some inductive biases inherent to CNNs, such as locality and translation equivariance, ViT cannot generalize well when trained on insufficient amounts of data. Compared with training ViT from scratch, transferring a large-scale pretrained one is more cost-efficient with better performance even when the target data are small scale. In addition, the cross-entropy (CE) loss is frequently utilized in scene classification yet has low robustness to noise labels and poor generalization performances for different scenes. In this article, a ViT-based model in combination with supervised contrastive learning (CL) is proposed, named ViT-CL. For CL, supervised contrastive (SupCon) loss, which is developed by extending the self-supervised contrastive approach to the fully supervised setting, can explore the label information of RSIs in embedding space and improve the robustness to common image corruption. In ViT-CL, a joint loss function that combines CE loss and SupCon loss is developed to prompt the model to learn more discriminative features. Also, a two-stage optimization framework is introduced to enhance the controllability of the optimization process of the ViT-CL model. Extensive experiments on the AID, NWPU-RESISC45, and UCM datasets verified the superior performance of ViT-CL, with the highest accuracies of 97.42%, 94.54%, and 99.76% among all competing methods, respectively.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A Building Shape Vectorization Hierarchy From VHR Remote Sensing Imagery
           Combined DCNNs-Based Edge Detection and PCA-Based Corner Extraction

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      Authors: Xiang Wen;Xing Li;Wenquan Han;Erzhu Li;Wei Liu;Lianpeng Zhang;Yihu Zhu;Shengli Wang;Sibao Hao;
      Pages: 750 - 761
      Abstract: The automatic vectorization of building shape from very high resolution remote sensing imagery is fundamental in many fields, such as urban management and geodatabase updating. Recently, deep convolutional neural networks (DCNNs) have been successfully used for building edge detection, but the results are raster images rather than vectorized maps and do not meet the requirements of many applications. Although there are some algorithms for converting raster images into vector maps, such vector maps often have too many vector points and irregular shapes. This article proposed a building shape vectorization hierarchy, which combined DCNNs-based building edge detection and a corner extraction algorithm based on principle component analysis for rapidly extracting building corners from the building edges. Experiments on the Jiangbei New Area Buildings and Massachusetts Buildings datasets showed that compared with the state-of-the-art corner detectors, the building vector corners extracted using our proposed algorithm had fewer breakpoints and isolated points, and our building vector boundaries were more complete and regular. In addition, the building shapes extracted using our hierarchy were 7.94% higher than the nonmaximum suppression method in terms of relaxed overall accuracy on the Massachusetts dataset. Overall, our proposed hierarchy is effective for building shape vectorization.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Neural Network Emulation of Synthetic Hyperspectral Sentinel-2-Like
           Imagery With Uncertainty

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      Authors: Miguel Morata;Bastian Siegmann;Adrián Pérez-Suay;José Luis García-Soria;Juan Pablo Rivera-Caicedo;Jochem Verrelst;
      Pages: 762 - 772
      Abstract: Hyperspectral satellite imagery provides highly resolved spectral information for large areas and can provide vital information. However, only a few imaging spectrometer missions are currently in operation. Aiming to generate synthetic satellite-based hyperspectral imagery potentially covering any region, we explored the possibility of applying statistical learning, i.e., emulation. Based on the relationship of a Sentinel-2 (S2) scene and a hyperspectral HyPlant airborne image, this work demonstrates the possibility to emulate a hyperspectral S2-like image. We tested the role of different machine learning regression algorithms and varied the image-extracted training dataset size. We found superior performance of neural network as opposed to the other algorithms when trained with large datasets (up to 100 000 samples). The developed emulator was then applied to the L2A (bottom-of-atmosphere reflectance) S2 subset, and the obtained S2-like hyperspectral reflectance scene was evaluated. The validation of emulated against reference spectra demonstrated the potential of the technique. $R^{2}$ values between 0.75 and 0.9 and NRMSE between 2 and 5% across the full 402–2356 nm range were obtained. Moreover, epistemic uncertainty is obtained using the dropout technique, revealing spatial fidelity of the emulated scene. We obtained highest SD values of 0.05 (CV of 8%) in clouds and values below 0.01 (CV of 7%) in vegetation land covers. Finally, the emulator was applied to an entire S2 tile (5490 × 5490 pixels) to generate a hyperspectral reflectance datacube with the texture of S2 (60 Gb, at a speed of 0.14 s/10000 pixels). As the emulator can convert any S2 tile into a hyperspectral image, such scenes give perspectives how future satellite imaging spectroscopy will look like.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • PCL–PTD Net: Parallel Cross-Learning-Based Pixel Transferred
           Deconvolutional Network for Building Extraction in Dense Building Areas
           With Shadow

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      Authors: Wuttichai Boonpook;Yumin Tan;Kritanai Torsri;Patcharin Kamsing;Peerapong Torteeka;Attawut Nardkulpat;
      Pages: 773 - 786
      Abstract: Urban building segmentation from remote sensed imageries is challenging because there usually exists a variety of building features. Furthermore, very high spatial resolution imagery can provide many details of the urban building, such as styles, small gaps among buildings, building shadows, etc. Hence, satisfactory accuracy in detecting and extracting urban features from highly detailed images still remains. Deep learning semantic segmentation using baseline networks works well on building extraction; however, their ability in building extraction in shadows area, unclear building feature, and narrow gaps among buildings in dense building zone is still limited. In this article, we propose parallel cross-learning-based pixel transferred deconvolutional network (PCL–PTD net), and then is used to segment urban buildings from aerial photographs. The proposed method is evaluated and intercompared with traditional baseline networks. In PCL–PTD net, it is composed of parallel network, cross-learning functions, residual unit in encoder part, and PTD in decoder part. The performance is applied to three datasets (Inria aerial dataset, international society for photogrammetry and remote sensing Potsdam dataset, and UAV building dataset), to evaluate its accuracy and robustness. As a result, we found that PCL–PTD net can improve learning capacities of the supervised learning model in differentiating buildings in dense area and extracting buildings covered by shadows. As compared to the baseline networks, we found that proposed network shows superior performance compared to all eight networks (SegNet, U-net, pyramid scene parsing network, PixelDCL, DeeplabV3+, U-Net++, context feature enhancement networ, and improved ResU-Net). The experiments on three datasets also demonstrate the ability of proposed framework and indicating its performance.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Joint Radio Frequency Interference and Deceptive Jamming Suppression
           Method for Single-Channel SAR via Subpulse Coding

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      Authors: Guoli Nie;Guisheng Liao;Cao Zeng;Xuepan Zhang;Dongchen Li;
      Pages: 787 - 798
      Abstract: The radio frequency interference (RFI) and deceptive jamming (DJ), as two major external threats to synthetic aperture radar (SAR) systems, can greatly reduce the readability and veracity of the obtained SAR images. Current interference suppression methods have no capability to suppress both of them. In this article, a subpulse coding (SPC)-based joint RFI and DJ suppression method for single-channel SAR systems is proposed. By making full use of the elaborate coding scheme and subpulse transmitting mode, SPC can effectively suppress RFIs in the Doppler domain. On the other hand, after the decoding process, by utilizing the subpulse digital beamforming (DBF) technology with the well-designed DBF weight vectors, DJs can also be suppressed greatly. Numerical experiments verify the effectiveness of the proposed method.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Pansharpening Based on Adaptive High-Frequency Fusion and Injection
           Coefficients Optimization

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      Authors: Yong Yang;Chenxu Wan;Shuying Huang;Hangyuan Lu;Weiguo Wan;
      Pages: 799 - 811
      Abstract: The purpose of pansharpening is to fuse a multispectral (MS) image with a panchromatic (PAN) image to generate a high spatial-resolution multispectral (HRMS) image. However, the traditional pansharpening methods do not adequately take consideration of the information of MS images, resulting in inaccurate detail injection and spectral distortion in the pansharpened results. To solve this problem, a new pansharpening approach based on adaptive high-frequency fusion and injection coefficients optimization is proposed, which can obtain an accurate injected high-frequency component (HFC) and injection coefficients. First, we propose a multi-level sharpening model to enhance the spatial information of the MS image, and then extract the HFCs from the sharpened MS image and PAN image. Next, an adaptive fusion strategy is designed to obtain the accurate injected HFC by calculating the similarity and difference of the extracted HFCs. Regarding the injection coefficients, we propose injection coefficients optimization scheme based on the spatial and spectral relationship between the MS image and PAN image. Finally, the HRMS image is obtained through injecting the fused HFC into the upsampled MS image with the injection coefficients. Experiments with simulated and real data are performed on IKONOS and Pléiades datasets. Both subjective and objective results indicate that our method has better performance than state-of-the-art pansharpening approaches.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Text-Image Matching for Cross-Modal Remote Sensing Image Retrieval via
           Graph Neural Network

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      Authors: Hongfeng Yu;Fanglong Yao;Wanxuan Lu;Nayu Liu;Peiguang Li;Hongjian You;Xian Sun;
      Pages: 812 - 824
      Abstract: The rapid development of remote sensing (RS) technology has produced massive images, which makes it difficult to obtain interpretation results by manual screening. Therefore, researchers began to develop automatic retrieval method of RS images. In recent years, cross-modal RS image retrieval based on query text has attracted many researchers because of its flexible and has become a new research trend. However, the primary problem faced is that the information of query text and RS image is not aligned. For example, RS images often have the attributes of multiscale and multiobjective, and the amount of information is rich, while the query text contains only a few words, and the information is scarce. Recently, graph neural network (GNN) has shown its potential in many tasks with its powerful feature representation ability. Therefore, based on GNN, this article proposes a new cross-modal RS feature matching network, which can avoid the degradation of retrieval performance caused by information misalignment by learning the feature interaction in query text and RS image, respectively, and modeling the feature association between the two modes. Specifically, to fuse the within-modal features, the text and RS image graph modules are designed based on GNN. In addition, in order to effectively match the query text and RS image, combined with the multihead attention mechanism, an image-text association module is constructed to focus on the parts related to RS image in the text. The experiments on two public standard datasets verify the competitive performance of the proposed model.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • An Analysis of Environmental Effect on VIIRS Nighttime Light Monthly
           Composite Data at Multiple Scales in China

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      Authors: Mengxin Yuan;Xi Li;Deren Li;Ji Wu;
      Pages: 825 - 840
      Abstract: Nighttime light (NTL) can provide valuable information about human activities. The temporal NTL variation has been previously explored, but the effect of environmental factors has not been fully considered. Here, this article focused on the environmental effect on NTL time series in China, using the visible infrared imaging radiometer suite (VIIRS) monthly products, Earth Observations Group (EOG) product, and Black Marble product, from January 2014 to December 2020. It was found that the NTL variations were statistically correlated with aerosols, vegetation, and surface albedo. NTL variations were negatively correlated with aerosol and vegetation, but positively correlated with surface albedo. Aerosol optical depth was important to explain the NTL variation among environmental factors. In 79% of urban areas in China, the adjusted R-squared of NTL and the three factors surpassed that of NTL and the two factors (vegetation and surface albedo) based on EOG product. In 60% of urban areas in China, the adjusted R-squared of NTL and the three factors surpassed that of NTL and the two factors (vegetation and surface albedo), based on Black Marble product. Both EOG monthly product and Black Marble monthly product were affected by aerosols, surface albedo, and vegetation at multiple scales. However, Black Marble product was less affected by aerosols than EOG product. This article suggests that environmental effect is crucial in the NTL variation. Understanding NTL temporal variation can improve the accuracy of time series VIIRS imagery for socioeconomic applications.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Global Sea Surface Height Measurement From CYGNSS Based on Machine
           Learning

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      Authors: Yun Zhang;Qi Lu;Qin Jin;Wanting Meng;Shuhu Yang;Shen Huang;Yanling Han;Zhonghua Hong;Zhansheng Chen;Weiliang Liu;
      Pages: 841 - 852
      Abstract: Cyclone Global Navigation Satellite System (CYGNSS) launched in recent years, provides a large amount of spaceborne GNSS Reflectometry data with all-weather, global coverage, high space-time resolution, and multiple signal sources, which provides new opportunities for the machine learning (ML) study of sea surface height (SSH) inversion. This article proposes for the first time two different CYGNSS SSH inversion models based on two widely used ML methods, back propagation (BP) neural network and convolutional neural network (CNN). The SSH calculated by using Danmarks Tekniske Universitet (DTU) 18 ocean wide mean SSH (MSSH) model (DTU18) with DTU global ocean tide model is used for verification. According to the strategy of independent analysis of data from different signal sources, the mean absolute error (MAE) of the BP and CNN models’ inversion specular points’ results during 7 days is 1.04 m and 0.63 m, respectively. The CLS 2015 product and Jason-3 data were also used for further validation. In addition, the generalization ability of the model, for 6 days and 13 days training sets, was also evaluated. For 6 days training set, the prediction results’ MAE of the BP model is 11.59 m and 5.90 m for PRN2 and PRN4, and the MAE of the CNN model is 1.37 m and 0.97 m for PRN2 and PRN4, respectively. The results show that BP and CNN inversions are in high agreement with each product, and the CNN model has relatively higher accuracy and better generalization ability.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Sensitivity Analysis of Microwave Spectrometer for Atmospheric Temperature
           and Humidity Sounding on the New Generation Fengyun Satellite

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      Authors: Wenming He;Zhenzhan Wang;Wenyu Wang;Zhou Zhang;
      Pages: 853 - 865
      Abstract: The vertical profiles, spatiotemporal distribution, and trends of temperature and humidity in the middle atmosphere are significant for numerical weather prediction and the analysis of global climate change. To better design and apply spaceborne microwave spectrometer on the new generation Fengyun satellite, the sensitivities of the spectrometers at 22.235, 50–60, 118.75, 183.31, 325.153, 380.33, 424.77, 448.0, and 556.94 GHz are analyzed, respectively. Qpack2 included in the atmospheric radiative transfer simulator is used. The results show that the retrieval accuracy of the 50.0–60.0 GHz spectrometer is obviously better than other spectrometers, and the effective detection height (EDH) is the highest, reaching 0.233 hPa. The humidity profiles bigger than 500 hPa are well detected by the seven channels of the Humidity and Temperature Profiler or 22.0–32.0 GHz spectrometer. The retrieval accuracy is better than 6%, which greatly improves the retrieval performance of humidity profiles in the lower troposphere over the sea. In addition, this frequency band is not affected by errors in sea surface temperature or wind speed. The humidity profiles over the sea and land in the middle atmosphere under clear-sky and cloudy-sky conditions are well detected by the 183.31 and 556.94 GHz spectrometer. The EDH can be increased to 1.14 hPa by the 556.94 GHz spectrometer. In the design and application of future spaceborne microwave spectrometers for temperature and humidity profiles detection, the spectrometers mentioned previously are good candidates, and the parameter configuration of these spectrometers can be used as a reference.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Foreword

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      Authors: Jón Atli Benediktsson;Melba Crawford;John Kerekes;Jie Shan;
      Pages: 866 - 867
      Abstract: The eleven papers in this special section serve as a tribute to Professor David A. Landgrebe who is known for his work in the fundamentals of multispectral image processing and analysis. The papers are grouped into three categories: historical and future developments, methodological advancements, and survey and review.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Assessing the Effects of Fuel Moisture Content on the 2018 Megafires in
           California

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      Authors: Zhenyu Kang;Xingwen Quan;Gengke Lai;
      Pages: 868 - 877
      Abstract: In 2018, the megafire episodes on record occurred in California, causing a large number of civilian deaths and damages. As an important part of the “fire environment triangle,” the fuel moisture content (FMC) of both live (LFMC) and dead (DFMC) vegetation were broadly accepted as the important drivers of wildfire ignition and spread, but their effects on the 2018 megafires in California were less explored. Here, we explored and compared the effects of LFMC and DFMC on the 2018 megafire in California, allowing for highlighting the role of different types of FMC in megafire risk assessment. The LFMC was collected from the global LFMC product. We used three indices obtained from the Canadian Forest Service Fire Weather Index Rating System as a proxy of DFMC products, including the fine fuel moisture code, duff moisture code (DMC), and drought code. We analyzed the long-term series (2001–2018) of these four indices in California to test whether these indices were indicative of the occurrence of the megafire, and which of the index was the most powerful driving the 2018 megafires. The results showed that all these indices were correlated with the fires in California. The LFMC showed the highest correlation with the fire occurrence between 2001 and 2018, whereas the DMC performed a major role in driving the 2018 megafire in California. This study presented the insights that the LFMC and DMC should be carefully considered in future operational fire risk assessments for megafire prescription, suppression, and response.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Tropical Cyclone Wind Direction Retrieval From Dual-Polarized SAR Imagery
           Using Histogram of Oriented Gradients and Hann Window Function

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      Authors: Weicheng Ni;Ad Stoffelen;Kaijun Ren;
      Pages: 878 - 888
      Abstract: Accurate knowledge of wind directions plays a critical role in ocean surface wind retrieval and tropical cyclone (TC) research. Under TC conditions, apparent wind streaks induced by marine atmospheric boundary layer rolls can be detected in VV- and VH-polarized synthetic aperture radar (SAR) images. It suggests that though relatively noisy, VH signals may help enhance wind streak orientation magnitudes contained in VV signals and thus to achieve a more accurate wind direction estimation. The study proposes a new method for wind direction retrieval from TC SAR images. Unlike conventional approaches, which calculate wind directions from single-polarization imagery, the method combines VV and VH signals to obtain continuous wind direction maps across moderate and extreme wind speed regimes. The technique is developed based on the histogram of oriented gradient descriptor and Hann window function, accounting for the contribution of neighboring wind streak information (weighted by separation distances). As a case study, the wind directions over four TCs (Karl, Maria, Douglas, and Larry) are derived and verified by estimates from simultaneous dropsonde, ASCAT and ECMWF winds, showing a promising consistency. Furthermore, a more comprehensive statistical analysis is carried out with 14 SAR images, revealing that obtained wind directions have a correlation coefficient of 0.98, a bias of −6.07$^{circ }$ and a RMSD of 20.24$^{circ }$, superior to estimates from VV (0.97, −7.84$^{circ }$, and 24.23$^{circ }$, resp.) and VH signals (0.96, −10.46$^{circ }$, and 29.53$^{circ }$, resp.). The encouraging results prove the feasibility of the technique in SAR wind direction retrieval.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A Discriminative Feature Learning Approach With Distinguishable Distance
           Metrics for Remote Sensing Image Classification and Retrieval

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      Authors: Zhiqi Zhang;Wen Lu;Xiaoxiao Feng;Jinshan Cao;Guangqi Xie;
      Pages: 889 - 901
      Abstract: The fast data acquisition rate due to the shorter revisit periods and wider observation coverage of satellites results in large amounts of remote sensing images every day. This brings the challenge of how to accurately search the images with similar visual content as the query image. Content-based image retrieval (CBIR) is a solution to this challenge, its performance heavily depends on the effectiveness of the image representation features and similarity evaluation metrics. Ideal image feature representations have dispersed interclass, compact intraclass distribution. However, the neural networks employed by many CBIR methods are trained with cross entropy loss, which does not directly optimize the metrics that evaluates interclass variance over intraclass variance, hence, their feature representations are suboptimal. Meanwhile, the traditional distance metrics used by many CBIR methods cannot index the similarity of feature representations well in high-dimensional space. For better CBIR performance, we propose a discriminative feature learning approach with distinguishable distance metrics for remote sensing image classification and retrieval. By balancing the diagonal elements and nondiagonal elements of the within-class scatter matrix of deep linear discriminant analysis, our proposed loss function, balanced deep linear discriminant analysis, can better optimize the Rayleigh–Ritz quotient, which measures interclass variance over intraclass variance. In addition, the proposed distance metrics, reciprocal exponential distance (RED), is more capable of maintaining distance contrast in high dimensionality, therefore, it can better index similarity for feature representations in high dimensionality. Both visual interpretations and quantitative metrics of extensive experiments demonstrated the effectiveness of our approach.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Lightweight Reconstruction of Urban Buildings: Data Structures,
           Algorithms, and Future Directions

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      Authors: Vivek Kamra;Prachi Kudeshia;Somaye ArabiNaree;Dong Chen;Yasushi Akiyama;Jiju Peethambaran;
      Pages: 902 - 917
      Abstract: Commercial buildings as well as residential houses represent core structures of any modern day urban or semiurban areas. Consequently, 3-D models of urban buildings are of paramount importance to a majority of digital urban applications, such as city planning, 3-D mapping and navigation, video games and movies, and construction progress tracking, among others. However, current studies suggest that existing 3-D modeling approaches often involve high computational cost and large storage volumes for processing the geometric details of the buildings. Therefore, it is essential to generate concise digital representations of urban buildings from the 3-D measurements or images so that the acquired information can be efficiently utilized for various urban applications. Such concise representations, often referred to as “lightweight” models, strive to capture the details of the physical objects with less computational storage. Furthermore, lightweight models consume less bandwidth for online applications and facilitate accelerated visualizations. With many emerging digital urban infrastructure applications, lightweight reconstruction is poised to become a new area of research in the urban remote sensing community. We aim to provide a thorough review of data structures, representations, and state-of-the-art algorithms for lightweight 3-D urban reconstruction. We discuss the strengths and weaknesses of key lightweight urban reconstruction techniques, ultimately providing guidance on future research prospects to fulfill the pressing needs of urban applications.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Generalized Fine-Resolution FPAR Estimation Using Google Earth Engine:
           Random Forest or Multiple Linear Regression

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      Authors: Yiting Wang;Yinggang Zhan;Guangjian Yan;Donghui Xie;
      Pages: 918 - 929
      Abstract: Accurate estimation of fine-resolution fraction of absorbed photosynthetically active radiation (FPAR) is urgently needed for modeling land surface processes at finer scales. While traditional methods can hardly balance universality, efficiency, and accuracy, methods using coarse-resolution products as a reference are promising for operational fine-resolution FPAR estimation. However, current methods confront major problems of underrepresentation of FPAR-reflectance relations within coarse-resolution FPAR products, particularly for densely vegetated areas. To overcome this limitation, this article has developed an enhanced scaling method that proposes an outlier removal procedure and a method weighting the selected samples and models FPAR through weighted multiple linear regression (MLR) between the coarse-resolution FPAR product and the aggregated fine-resolution surface reflectance. Meanwhile, a random forest regression (RFR) method has also been implemented for comparison. Both methods were particularly applied to Landsat 8 OLI and moderate resolution imaging spectroradiometer (MODIS) FPAR data on the Google earth engine. Their performance was tested on a regional scale for an entire year. The results of the enhanced scaling method were closer to the in situ measurements (RMSE = 0.058 and R2 = 0.768) and were more consistent with the MODIS FPAR (RMSE = 0.091 and R2 = 0.894) than those of the RFR, particularly over densely vegetated pixels. This indicates that a well-designed simple MLR-based method can outperform the more sophisticated RFR method. The enhanced scaling method is also less sensitive to the number of training samples than the RFR method. Moreover, both methods are insensitive to land cover maps, and their computation efficiency depends on the number of images to be estimated.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Dual-Resolution and Deformable Multihead Network for Oriented Object
           Detection in Remote Sensing Images

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      Authors: Donghang Yu;Qing Xu;Xiangyun Liu;Haitao Guo;Jun Lu;Yuzhun Lin;Liang Lv;
      Pages: 930 - 945
      Abstract: Compared with general object detection, the scale variations, arbitrary orientations, and complex backgrounds of objects in remote sensing images make it more challenging to detect oriented objects. Especially for oriented objects that have large aspect ratios, it is more difficult to accurately detect their boundary. Many methods show excellent performance on oriented object detection, most of which are anchor-based algorithms. To mitigate the performance gap between anchor-free algorithms and anchor-based algorithms, this article proposes an anchor-free algorithm called dual-resolution and deformable multihead network (DDMNet) for oriented object detection. Specifically, the dual-resolution network with bilateral fusion is adopted to extract high-resolution feature maps which contain both spatial details and multiscale contextual information. Then, the deformable convolution is incorporated into the network to alleviate the misalignment problem of oriented object detection. And a dilated feature fusion module is performed on the deformable feature maps to expand their receptive fields. Finally, box boundary-aware vectors instead of the angle are leveraged to represent the oriented bounding box and the multihead network is designed to get robust predictions. DDMNet is a single-stage oriented object detection method without using anchors and exhibits promising performance on the public challenging benchmarks. DDMNet obtains 90.49%, 93.25%, and 78.66% mean average precision on the HRSC2016, FGSD2021, and DOTA datasets. In particular, DDMNet achieves 79.86% at mAP75 and 53.85% at mAP85 on the HRSC2016 dataset, respectively, outperforming the current state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Hyperspectral Anomaly Detection via Sparse Representation and
           Collaborative Representation

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      Authors: Sheng Lin;Min Zhang;Xi Cheng;Kexue Zhou;Shaobo Zhao;Hai Wang;
      Pages: 946 - 961
      Abstract: Sparse representation (SR)-based approaches and collaborative representation (CR)-based methods are proved to be effective to detect the anomalies in a hyperspectral image (HSI). Nevertheless, the existing methods for achieving hyperspectral anomaly detection (HAD) generally only consider one of them, failing to comprehensively exploit them to further promote the detection performance. To address the issue, a novel HAD method, which integrates both SR and CR, is proposed in this article. To be specific, an SR model, whose overcomplete dictionary is generated by means of the density-based clustering algorithm and superpixel segmentation method, is first constructed for each pixel in an HSI. Then, for each pixel in an HSI, the used atoms in SR model are sifted to form the background dictionary corresponding to the CR model. To fully exploit both SR and CR information, we further combine the residual features obtained from both SR and CR model by the nonlinear transformation function to generate the response map. Finally, to preserve contour information of the objects, a postprocessing operation with guided filter is imposed into the response map to acquire the detection result. Experiments conducted on simulated and real datasets demonstrate that the proposed SRCR outperforms the state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Super-Resolution-Aided Sea Ice Concentration Estimation From AMSR2 Images
           by Encoder–Decoder Networks With Atrous Convolution

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      Authors: Tiantian Feng;Xiaomin Liu;Rongxing Li;
      Pages: 962 - 973
      Abstract: Passive microwave data is an important data source for the continuous monitoring of Arctic-wide sea ice concentration (SIC). However, its coarse spatial resolution leads to blurring effects at the ice-water divides, resulting in the great challenges of fine-scale and accurate SIC estimation, especially for regions with low SIC. Besides, the SIC derived by operational algorithms using high-frequency passive microwave observations has great uncertainties in open water or marginal ice zones due to atmospheric effects. In this article, a novel framework is proposed to achieve accurately SIC estimation with improved spatial details from original low-resolution Advanced Microwave Scanning Radiometer 2 (AMSR2) images, with joint the super-resolution (SR) and SIC estimation network. Based on the SR network, the spatial resolution of original AMSR2 images can be improved by four times, benefiting to construct AMSR2 SR features with more high-frequency information for SIC estimation. The SIC network with an encoder–decoder structure and atrous convolution, is employed to accurately perform the SIC retrieval by considering the characteristics of passive microwave images in the Arctic sea ice region. Experimental results show that the proposed SR-Aided SIC estimation approach can generate accurate SIC with more detailed sea ice textures and much sharper sea ice edges. With respect to MODIS SIC products distributed in Arctic scale, the proposed model achieves a root-mean-square error (RMSE) of 5.94% and mean absolute error (MAE) of 3.04%, whereas the Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) SIC results have three and two times greater values of RMSE and MAE.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Determination and Sensitivity Analysis of the Specular Reflection Point in
           GNSS Reflectometry

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      Authors: Jyh-Ching Juang;
      Pages: 974 - 982
      Abstract: In applying Global Navigation Satellite System Reflectometry (GNSS-R) techniques for the remote sensing of surface properties of the Earth, it is imperative to determine the specular reflection point and provide a quantitative characterization of the associated errors. In this article, a rigorous formulation of the problem with respect to ellipsoidal Earth is provided for the determination and error analysis of the specular reflection point. A polynomial equation approach is developed to characterize the specular reflection point. This explicit characterization is beneficial in the GNSS-R receiver operation. The sensitivity analysis is further performed to assess the errors in the presence of uncertainties.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Capturing Small Objects and Edges Information for Cross-Sensor and
           Cross-Region Land Cover Semantic Segmentation in Arid Areas

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      Authors: Panli Yuan;Qingzhan Zhao;Yuchen Zheng;Xuewen Wang;Bin Hu;
      Pages: 983 - 997
      Abstract: In the oasis area adjacent to the desert, there is more complex land cover information with rich details, multiscales of interest objects, and blur edge information, which poses some challenges to the semantic segmentation task in remote sensing images (RSIs). In traditional semantic segmentation methods, detailed spatial information is more likely lost in feature extraction stage and the global context information is more effectively integrated into segmentation results. To overcome these land cover semantic segmentation model, FPN_PSA_DLV3+ network, is proposed in an encoder–decoder manner capturing more fine edge and small objects information in RSIs. In the encoder stage, the improved atrous spatial pyramid pooling module extracts the multiscale features, especially small-scale feature details; feature pyramid network (FPN) module realizes better integration of detailed information and semantic information; and the spatial context information at both global and local levels is enhanced by introducing polarized self-attention (PSA) module. For the decoder stage, the FPN_PSA_DLV3+ network further adds a feature fusion branch to concatenate more low-level features. We select Landsat5/7/8 satellite RSIs from the areas of north and south of Xinjiang. Then, three self-annotated time-series datasets with more small objects and fine edges information are constructed by data augmentation. The experimental results show that the proposed method improves the segmentation performance of small targets and edges, and the classification performance increases from 81.55% to 83.10% F1 score and from 72.65% to 74.82% mean intersection over union only using red–green–blue bands. Meanwhile, the FPN_PSA_DLV3+ network s-ows great generalization in cross region and cross sensor.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural
           Network

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      Authors: Yangyang Chen;Dongping Ming;Junchuan Yu;Lu Xu;Yanni Ma;Yan Li;Xiao Ling;Yueqin Zhu;
      Pages: 998 - 1018
      Abstract: Automatic landslide detection based on very high spatial resolution remote sensing images is crucial for disaster prevention and mitigation applications. With the rapid development of deep-learning techniques, state-of-the-art semantic segmentation methods based on fully convolutional network (FCNN) have achieved outstanding performance in the landslide detection task. However, most of the existing articles only utilize visual features. Even if the advanced FCNN models are applied, there is still a certain amount of falsely detected and miss detected landslides. In this article, we innovatively introduce landslide susceptibility as prior knowledge and propose an innovative susceptibility-guided landslide detection method based on FCNN (SG-FCNN) to detect landslides from single temporal images. In addition, an unsupervised change detection method based on the mean changing magnitude of objects (MCMO) is further proposed and integrated with the SG-FCNN to detect newly occurred landslides from bitemporal images. The effectiveness of the proposed SG-FCNN and MCMO has been tested in Lantau Island, Hong Kong. The experimental results show that the SG-FCNN can significantly reduce the amount of falsely detected and miss detected landslides compared with the FCNN. It can conclude that applying landslide susceptibility as prior knowledge is much more effective than using visual features only, which introduces a new methodology of landslide detection and lifts the detection performance to a new level.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • High-Resolution Planetscope Imagery and Machine Learning for Estimating
           Suspended Particulate Matter in the Ebinur Lake, Xinjiang, China

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      Authors: Pan Duan;Fei Zhang;Changjiang Liu;Mou Leong Tan;Jingchao Shi;Weiwei Wang;Yunfei Cai;Hsiang-Te Kung;Shengtian Yang;
      Pages: 1019 - 1032
      Abstract: Ebinur Lake is a shallow lake and vulnerable to strong winds, which can lead to drastic changes in suspended particulate matter (SPM). High spatial and temporal resolution images are therefore urgently needed for SPM monitoring over the Ebinur Lake. Hence, a high-efficiency inversion model of estimating SPM from high-resolution images using machine learning is essential to increase the amount of extracted information through band combinations quadratic optimization. This article aims to evaluate the capability of the PlanetScope images and four machine learning approaches for estimating SPM of the Ebinur Lake. The specific objectives include: to obtain the sensitive bands and band combinations for SPM using correlation analysis; to quadratically optimize the combination pattern of sensitive bands using a linear model; and to compare the accuracy of traditional linear model and machine learning models in estimating SPM. The results of the study confirm that after linear model quadratic optimization, the band combinations of B3*B4, (B2+B3)/ (B2-B3), (B3+B4)*(B3+B4), and (B3-B2)/(B2/B3) have higher accuracy than that of the single band model. By inputting the preferred four-band combinations into the partial least squares, random forest, extreme gradient boosting, gradient boosting decision tree, and categorical boosting (CatBoost) models, the performance of the SPM inversion based on PlanetScope images is better than the traditional linear model. Validation of the inversion maps with observations further indicates that the CatBoost model performed the best.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A New Algorithm for Measuring Vegetation Growth Using GNSS Interferometric
           Reflectometry

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      Authors: Jie Li;Dongkai Yang;Feng Wang;Xuebao Hong;
      Pages: 1033 - 1041
      Abstract: The use of global navigation satellite system interferometric reflectometry (GNSS-IR) to measure vegetation growth status has become a rapidly growing technique in remote sensing. GNSS signals reflected by the soil surface affect the accuracy of vegetation growth status (vegetation cover density) measurement, and the influence of soil moisture (SM) varies. This study establishes a calibration model that can reduce the influence of SM and snow layer on reflectivity. We used a direct-reflected signal amplitude ratio and GNSS-IR altimeter based on the Lomb–Scargle Periodogram to calculate the reflectivity of vegetation and snow layer depth. GNSS data from plate boundary observation were used to verify the validity of our model. The results show that reflectivity correlates better with vegetation growth status after calibrating the influence of the SM and snow layer. Moreover, the correlation increased by nearly 0.14. This study analyzed the influence of the snow layer and found that it had a noticeable effect on vegetation growth status measurement when the snow depth was over 30 cm. Furthermore, a fusion method is proposed to improve the accuracy of vegetation growth status measurement by combining the reflectivity and normalized microwave reflection index (NMRI). The experimental results show that better performance can be obtained compared to the single observation of the reflectivity and NMRI, and the best correlation between the measured and in situ normalized difference vegetation index is over 0.91, and the root mean square error decreases to 0.1893.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for
           SAR Ship Detection

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      Authors: Lin Bai;Cheng Yao;Zhen Ye;Dongling Xue;Xiangyuan Lin;Meng Hui;
      Pages: 1042 - 1056
      Abstract: Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A Lightweight Multitask Learning Model With Adaptive Loss Balance for
           Tropical Cyclone Intensity and Size Estimation

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      Authors: Wei Tian;Xinxin Zhou;Xianhua Niu;Linhong Lai;Yonghong Zhang;Kenny Thiam Choy Lim Kam Sian;
      Pages: 1057 - 1071
      Abstract: Accurate tropical cyclone (TC) intensity and size estimation are key in disaster management and prevention. While great breakthroughs have been made in TC intensity estimation research, there is currently a lack of research on TC size reflecting TC influence radius. Therefore, we propose a lightweight multi-task learning model (TC-MTLNet) with adaptive loss balance to simultaneously estimate TC intensity and size. Adaptive loss balance is utilized to solve the problem of inconsistent convergence speed of TC intensity and size estimation tasks. The model based on four 2-D convolutions, four 3-D convolutions and three fully connected layers takes up less computational and storage space and improves the accuracy of TC intensity and size estimation by sharing knowledge among multiple tasks. In addition, due to the imbalanced distribution of TC samples, with significantly few low-intensity and high-intensity TC satellite data, this phenomenon poses a great challenge to TC intensity and size estimation. So, we utilize the influence of nearby samples to calibrate the sample density to weight the loss function to enable the model to be generalized to all samples. The result shows that the root-mean-square error (RMSE) of TC intensity estimation is $text{8.40},text{kts}$, which is 33.5% lower than that of the Advanced Dvorak Technique (ADT) and 11.4% lower than that of the deep learning method (3DAttentionTCNet). The mean absolute error (MAE) of the TC size estimation is $text{20.89},text{nmi}$, which is a 16% reduction compared to the Multi-Platform Tropical Cyclone Surface Winds Analysis (MTCSWA).
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Hyperspectral Image Classification Based on 3-D Multihead Self-Attention
           Spectral–Spatial Feature Fusion Network

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      Authors: Qigao Zhou;Shuai Zhou;Feng Shen;Juan Yin;Dingjie Xu;
      Pages: 1072 - 1084
      Abstract: Convolutional neural networks are a popular method in hyperspectral image classification. However, the accuracy of the models is closely related to the number and spatial size of training samples. Which relieve the performance decline by the number and spatial size of training samples, we designed a 3-D multihead self-attention spectral–spatial feature fusion network (3DMHSA-SSFFN) that contains step-by-step feature extracted blocks (SBSFE) and 3-D multihead-self-attention-module (3DMHSA). The proposed step-by-step feature extracted blocks relieved the declining-accuracy phenomenon for the limited number of training samples. Multiscale convolution kernels extract more spatial–spectral features in the step-by-step feature-extracted blocks. In hyperspectral image classification, the 3DMHSA module enhances the stability of classification by correlating disparate features. Experimental results show that 3DMHSA-SSFFN possesses a better classification performance than other advanced models through the limited number of balance and imbalance training data in three data.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Bag-of-Features-Driven Spectral-Spatial Siamese Neural Network for
           Hyperspectral Image Classification

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      Authors: Zhaohui Xue;Tianzhi Zhu;Yiyang Zhou;Mengxue Zhang;
      Pages: 1085 - 1099
      Abstract: Deep learning (DL) exhibits commendable performance in hyperspectral image (HSI) classification because of its powerful feature expression ability. Siamese neural network further improves the performance of DL models by learning similarities within-class and differences between-class from sample pairs. However, there are still some limitations in siamese neural network. On the one hand, siamese neural network usually needs a large number of negative pair samples in the training process, leading to computing overhead. On the other hand, current models may lack interpretability because of complex network structure. To overcome the above limitations, we propose a spectral-spatial siamese neural network with bag-of-features (S3BoF) for HSI classification. First, we use a siamese neural network with 3-D and 2-D convolutions to extract the spectral-spatial features. Second, we introduce stop-gradient operation and prediction head structure to make the siamese neural network work without negative pair samples, thus reducing the computational burden. Third, a bag-of-features (BoF) learning module is introduced to enhance the model interpretability and feature representation. Finally, a symmetric loss and a cross entropy loss are respectively used for contrastive learning and classification. Experiments results on four common hyperspectral datasets indicated that S3BoF performs better than the other traditional and state-of-the-art deep learning HSI classification methods in terms of classification accuracy and generalization performance, with improvements in terms of OA around 1.40%–30.01%, 0.27%–8.65%, 0.37%–6.27%, 0.22%–6.64% for Indian Pines, University of Pavia, Salinas, and Yellow River Delta datasets, respectively, under 5% -abeled samples per class.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using
           Radiance-Based Split-Window Algorithm

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      Authors: Mengmeng Wang;Miao Li;Zhengjia Zhang;Tian Hu;Guojin He;Zhaoming Zhang;Guizhou Wang;Hua Li;Junlei Tan;Xiuguo Liu;
      Pages: 1100 - 1112
      Abstract: The thermal infrared sensor-2 (TIRS-2) carried on Landsat 9 is the newest thermal infrared (TIR) sensor for the Landsat project and provides two adjacent TIR bands, which greatly benefits the land surface temperature (LST) retrieval at high spatial resolution. In this article, a radiance based split window (RBSW) algorithm for retrieving LST from Landsat 9 TIRS-2 data was proposed. In addition, the split-window covariance-variance ratio (SWCVR) algorithm was improved and applied to Landsat 9 TIRS-2 data for estimating atmospheric water vapor (AWV) that is required for accurate LST retrieval. The performance of the proposed method was assessed using the simulation data and satellite observations. Results reveal that the retrieved LST using the RBSW algorithm has a bias of 0.06 K and root-mean-square error (RMSE) of 0.51 K based on validation with the simulation data. The sensitivity analysis exhibited a LST error of
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Self-Filtered Learning for Semantic Segmentation of Buildings in Remote
           Sensing Imagery With Noisy Labels

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      Authors: Hunsoo Song;Lexie Yang;Jinha Jung;
      Pages: 1113 - 1129
      Abstract: Not all building labels for training improve the performance of the deep learning model. Some labels can be falsely labeled or too ambiguous to represent their ground truths, resulting in poor performance of the model. For example, building labels in OpenStreetMap (OSM) and Microsoft Building Footprints (MBF) are publicly available training sources that have great potential to train deep models, but directly using those labels for training can limit the model's performance as their labels are incomplete and inaccurate, called noisy labels. This article presents self-filtered learning (SFL) that helps a deep model learn well with noisy labels for building extraction in remote sensing images. SFL iteratively filters out noisy labels during the training process based on loss of samples. Through a multiround manner, SFL makes a deep model learn progressively more on refined samples from which the noisy labels have been removed. Extensive experiments with the simulated noisy map as well as real-world noisy maps, OSM and MBF, showed that SFL can improve the deep model's performance in diverse error types and different noise levels.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Local Information Interaction Transformer for Hyperspectral and LiDAR Data
           Classification

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      Authors: Yuwen Zhang;Yishu Peng;Bing Tu;Yaru Liu;
      Pages: 1130 - 1143
      Abstract: The multisource remote sensing classification task has two main challenges. 1) How to capture hyperspectral image (HSI) and light detection and ranging (LiDAR) features cooperatively to fully mine the complementary information between data. 2) How to adaptively fuse multisource features, which should not only overcome the imbalance between HSI and LiDAR data but also avoid the generation of redundant information. The local information interaction transformer (LIIT) model proposed herein can effectively address these above issues. Specifically, multibranch feature embedding is first performed to help in the fine-grained serialization of multisource features; subsequently, a local-based multisource feature interactor (L-MSFI) is designed to explore HSI and LiDAR features together. This structure provides an information transmission environment for multibranch features and further alleviates the homogenization processing mode of the self-attention process. More importantly, a multisource feature selection module (MSTSM) is developed to dynamically fuse HSI and LiDAR features to solve the problem of insufficient fusion. Experiments were carried out on three multisource remote-sensing classification datasets, the results of which show that LIIT has more performance advantages than the state-of-the-art CNN and transformer methods.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Optimized Views Photogrammetry: Precision Analysis and a Large-Scale Case
           Study in Qingdao

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      Authors: Qingquan Li;Hui Huang;Wenshuai Yu;San Jiang;
      Pages: 1144 - 1159
      Abstract: Unmanned aerial vehicle (UAVs) have become one of the widely used remote sensing platforms and played a critical role in the construction of smart cities. However, due to the complex environment in urban scenes, secure, and accurate data acquisition brings great challenges to 3-D modeling and scene updating. Optimal trajectory planning of UAVs and accurate data collection of onboard cameras are nontrivial issues in urban modeling. This study presents the principle of optimized views photogrammetry and verifies its precision and potential in large-scale 3-D modeling. Different from oblique photogrammetry, optimized views photogrammetry uses rough models to generate and optimize UAV trajectories, which is achieved through the consideration of model point reconstructability and view point redundancy. Based on the principle of optimized views photogrammetry, this study first conducts a precision analysis of 3-D models by using UAV images of optimized views photogrammetry and then executes a large-scale case study in the urban region of Qingdao City, China, to verify its engineering potential. By using GCPs for image orientation precision analysis and terrestrial laser scanning (TLS) point clouds for model quality analysis, experimental results show that optimized views photogrammetry could construct stable image connection networks and could achieve comparable image orientation accuracy. Benefiting from the accurate image acquisition strategy, the quality of mesh models significantly improves, especially for urban areas with serious occlusions, in which 3 to 5 times of higher accuracy has been achieved. Besides, the case study in Qingdao City verifies that optimized views photogrammetry can be a reliable and powerful solution for the large-scale 3-D modeling in complex urban scenes.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Dynamic Soft Label Assignment for Arbitrary-Oriented Ship Detection

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      Authors: Yangfan Li;Chunjiang Bian;Hongzhen Chen;
      Pages: 1160 - 1170
      Abstract: Ship detection with several military and civilian applications has drawn considerable attention in recent years. In remote sensing images, ships have the characteristics of arbitrary orientation. Based on the characteristics, many arbitrary-oriented ship detectors have been proposed. Most of these detectors preset many horizontal or rotated anchors and determine the positive and negative samples based on the intersection over union (IoU) between the anchor and ground-truth bounding box, in what is called the label assignment process. However, IoU performance is limited as it can only reflect the quality of the anchor to a certain extent. In addition, the manually fixed IoU threshold to separate the positive and negative limits the flexibility of the method, as different ships may have different optimal thresholds. Moreover, the equally weighted training samples cause a misalignment between the classification and regression heads. Therefore, we propose a dynamic soft label assignment method for arbitrary-oriented ship detection. First, we design a novel anchor quality score function that takes into account both prior and prediction information of the anchor and enables the model to participate in the label assignment process. Second, we propose a dynamic anchor quality score threshold instead of a fixed IoU threshold for dividing positive and negative samples. Third, in contrast to assigning equal weights, we propose a soft label assignment strategy to weigh the training samples in the loss function. The proposed method offers superior detection performances for arbitrary-oriented ships with only one horizontal preset anchor. Experimental results on HRSC2016, FGSD, and ShipRSImageNet datasets demonstrate the effectiveness of our proposed dynamic soft label assignment for arbitrary-oriented ship detection.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Attention-Aware Deep Feature Embedding for Remote Sensing Image Scene
           Classification

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      Authors: Xiaoning Chen;Zonghao Han;Yong Li;Mingyang Ma;Shaohui Mei;Wei Cheng;
      Pages: 1171 - 1184
      Abstract: Due to the wide application of remote sensing (RS) image scene classification, more and more scholars activate great attention to it. With the development of the convolutional neural network (CNN), the CNN-based methods of the RS image scene classification have made impressive progress. In the existing works, most of the architectures just considered the global information of the RS images. However, the global information contains a large number of redundant areas that diminish the classification performance and ignore the local information that reflects more fine spatial details of local objects. Furthermore, most CNN-based methods assign the same weights to each feature vector causing the mode to fail to discriminate the crucial features. In this article, a novel method by Two-branch Deep Feature Embedding (TDFE) with a dual attention-aware (DAA) module for RS image scene classification is proposed. In order to mine more complementary information, we extract global semantic-based features of high level and local object-based features of low level by the TDFE module. Then, to focus selectively on the key global-semantics feature maps as well as the key local regions, we propose a DAA module to attain those key information. We conduct extensive experiments to verify the superiority of our proposed method, and the experimental results obtained on two widely used RS scene classification benchmarks demonstrate the effectiveness of the proposed method.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Filtering Specialized Change in a Few-Shot Setting

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      Authors: Martin Hermann;Sudipan Saha;Xiao Xiang Zhu;
      Pages: 1185 - 1196
      Abstract: The aim of change detection in remote sensing usually is not to find all differences between the observations, but rather only specific types of change, such as urban development, deforestation, or even more specialized categories like roadwork. However, often there are no large public datasets available for very fine-grained tasks, and to collect the amount of training data needed for most supervised learning methods is very costly and often prohibitive. For this reason, we formulate the problem of few-shot filtering, where we are provided with a relatively large change detection dataset and, at test time, a few instances of one particular change type that we try to “filter out” of the learned changes. For example, we might train on data of general urban change, and, given some samples of building construction, aim to only predict instances of these on the test set, all without any explicit labels for buildings in the training data. We further investigate a fine-tuning approach to this problem and assess its performance on a public dataset that we adapt to be used in this novel setting.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Query by Example in Remote Sensing Image Archive Using Enhanced Deep
           Support Vector Data Description

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      Authors: Omid Ghozatlou;Miguel Heredia Conde;Mihai Datcu;
      Pages: 1197 - 1210
      Abstract: This article studies remote sensing image retrieval using kernel-based support vector data description (SVDD). We exploit deep SVDD, which is a well-known method for one-class classification to recover the most relevant samples from the archive. To this end, a deep neural network (DNN) is jointly trained to map the data into a hypersphere of minimum volume in the latent space. It is expected that similar samples to the query are compressed inside of the hypersphere. The closest embedding to the center of the hypersphere is related to the most relevant sample to query. We enhance deep SVDD by injecting the statistical information of data to the DNN by means of additional terms in the cost function. The first enhancement method takes advantage of covariance regularization of batches of the training set to penalize unnecessary redundancy and minimize the correlation between the different dimensions of the embedding. The second method involves unlocking the hypersphere's predefined center while preventing network divergence during training. Therefore, two parameters are designed to control the importance of the drifting of the center and the importance of a fixed predefined center (convergence), respectively. This has been implemented by considering the average of batches of embedding in each iteration as the updated center. This pushes irrelevant samples away from query samples, making data clustering easier for the DNN. The performance of the proposed methods is evaluated on benchmark datasets.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A CNN-Transformer Hybrid Model Based on CSWin Transformer for UAV Image
           Object Detection

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      Authors: Wanjie Lu;Chaozhen Lan;Chaoyang Niu;Wei Liu;Liang Lyu;Qunshan Shi;Shiju Wang;
      Pages: 1211 - 1231
      Abstract: The object detection of unmanned aerial vehicle (UAV) images has widespread applications in numerous fields; however, the complex background, diverse scales, and uneven distribution of objects in UAV images make object detection a challenging task. This study proposes a convolution neural network transformer hybrid model to achieve efficient object detection in UAV images, which has three advantages that contribute to improving object detection performance. First, the efficient and effective cross-shaped window (CSWin) transformer can be used as a backbone to obtain image features at different levels, and the obtained features can be input into the feature pyramid network to achieve multiscale representation, which will contribute to multiscale object detection. Second, a hybrid patch embedding module is constructed to extract and utilize low-level information such as the edges and corners of the image. Finally, a slicing-based inference method is constructed to fuse the inference results of the original image and sliced images, which will improve the small object detection accuracy without modifying the original network. Experimental results on public datasets illustrate that the proposed method can improve performance more effectively than several popular and state-of-the-art object detection methods.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • A High-Efficiency Spectral Element Method Based on CFS-PML for GPR
           Numerical Simulation and Reverse Time Migration

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      Authors: Xun Wang;Tianxiao Yu;Deshan Feng;Siyuan Ding;Bingchao Li;Yuxin Liu;Zheng Feng;
      Pages: 1232 - 1243
      Abstract: Improving the accuracy and efficiency of the numerical simulation of ground penetrating radar (GPR) becomes a pressing need with the rapidly increased amount of inversion data and the growing demand for migration imaging quality. In this article, we present a numerical spectral element time-domain (SETD) simulation procedure for GPR forward modeling and further apply it to the reverse time migration (RTM) with complex geoelectric models. This approach takes into account the flexibility of the finite element methods and the high precision of the spectral methods. Meanwhile, in this procedure, the complex frequency shifted perfectly matched layer (CFS-PML) is loaded to effectively suppress the echo at the truncated boundary, and the per-element GPU parallel framework used can achieve up to 5.7788 times the efficiency compared with the CPU calculation. The experiments on SETD spatial convergence and CFS-PML optimal parameter selection showed that, under the same degree of freedom, the SETD offered substantially better accuracy compared with the traditional FETD. The experiments on RTM of different profiles with different orders of SETD via a complex geoelectric model verify the universality of the algorithm. The results indicate that the RTM imaging effect has been significantly improved with the increase of SETD order. It fully proves the great potential of efficient and high-precision SETD simulation algorithm in the RTM imaging direction and shows certain guiding significance for underground target structure exploration.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Estimation of European Terrestrial Ecosystem NEP Based on an Improved CASA
           Model

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      Authors: Siyi Qiu;Liang Liang;Qianjie Wang;Di Geng;Junjun Wu;Shuguo Wang;Bingqian Chen;
      Pages: 1244 - 1255
      Abstract: Net ecosystem productivity (NEP) is a key indicator to describe terrestrial ecosystem functions and carbon sinks. The CASA model was improved by optimizing the parameters optimum temperature and maximum light use efficiency (${varepsilon }_{max }$), and the NEP value of the European terrestrial ecosystem was calculated by combining the soil respiration model. The results showed that when using vegetation classification data to optimize parameter ${varepsilon }_{max }$, the R2 of NEP between estimates and observations increased from 0.252 to 0.403, and the RMSE decreased from 84.557 to 64.466 gC·m−2·month−1. After further optimizing the optimum temperature, R2 increased to 0.428, and the RMSE decreased to 63.720 gC·m−2·month−1. It indicated that the CASA model could be improved by optimizing ${varepsilon }_{max }$ as well as optimum temperature, which was a good approach to improve the NEP estimations. Based on this, the NEP spatiotemporal changes in various regions of Europe were analyzed using the optimization results. The NEP values of European terrestrial ecosystem has regional differences, showing a pattern of western region> southern region> central region> eastern region> northern region. The monthly change of NEP in each region is a single peak curve with high in summer and low in winter, and the annual overall value is positive (i.e., it shows a carbon sink). The research can enable us to obtain the carbon source/-ink distribution information in Europe more accurately and provide a scientific reference for carbon balance policy formulation in the region.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Optical and SAR Image Dense Registration Using a Robust Deep Optical Flow
           Framework

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      Authors: Han Zhang;Lin Lei;Weiping Ni;Xiaoliang Yang;Tao Tang;Kenan Cheng;Deliang Xiang;Gangyao Kuang;
      Pages: 1269 - 1294
      Abstract: The coregistration of optical and synthetic aperture radar (SAR) imageries is the bottleneck in exploring the complementary information from the two multimodal datasets. The difficulties lie in not only the complex radiometric relationship between them, but also the distinct geometrical models of the optical and SAR imaging systems, which cause it nontrivial to explicitly depict the spatial relationship between the corresponding image regions when elevation fluctuations exist. This article aims to investigate the optical flow technique for the pixelwise dense registration of the high-resolution optical and SAR images, so as to get rid of the outlier removal and geometric mapping procedures, which have to be conducted in the classical image registration approaches that are based on sparse feature point matching. Herein, a deep optical flow framework is designed. First, a dilated feature concatenation method is proposed to enhance the discriminability of the pixelwise features for similarity measurement. An effective network training strategy is used, based on a smoothed flow loss, and also a training dataset that contains simulated elevation fluctuations. Second, we propose a self-supervised optical flow fine-tuning strategy. It incorporates the strength of the blockwise matching approach, which produces better matching precision, into the proposed pixelwise matching procedure. In this way, the accuracy of the optical-SAR dense registration is substantially improved. Extensive experiments conducted on the 1-m resolution optical-SAR image pairs of different land-cover types and distinct topographic conditions indicate that the proposed optical-SAR optical flow network -Ft framework is quite robust, and has the potential to perform the optical-SAR image dense registration in practical applications. The Python code of the proposed deep optical flow network will be made available.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Squint Spotlight SAR Imaging by Two-Step Scaling Transform-Based Extended
           PFA and 2-D Autofocus

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      Authors: Shengliang Han;Daiyin Zhu;Xinhua Mao;
      Pages: 1295 - 1307
      Abstract: In this article, a novel imaging algorithm by combining two-step scaling transform (TSST) with structure-aided 2-D autofocus is proposed for the squint spotlight synthetic aperture radar (SAR). First, on the basis of planar wavefront assumption, a modified range-frequency linear scaling transform (MRFLST) and an azimuth-time nonlinear scaling transform (ATNST) are proposed to eliminate the coupling between range-frequency and azimuth-time of the received echo. Furthermore, to improve the efficiency, the MRFLST is implemented by using the principle of chirp scaling (PCS), which involves only complex multiplications and fast Fourier transforms (FFTs) without any interpolation, meanwhile, a constant scaling factor (CSF) selecting criteria is defined to avoid range spectrum aliasing. Then, to correct the phase error caused by the range measurement error and atmospheric propagation effects, the prior 2-D phase error structure implied in the TSST is analyzed. Finally, by integrating the derived 2-D phase error structure and range frequency fragmentation technique, a new 2-D autofocus algorithm is presented to improve the image quality. Simulated and real data experiments are carried out to verify the proposed algorithm.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Spectral–Temporal Fusion of Satellite Images via an End-to-End
           Two-Stream Attention With an Effective Reconstruction Network

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      Authors: Tayeb Benzenati;Yousri Kessentini;Abdelaziz Kallel;
      Pages: 1308 - 1320
      Abstract: Due to technical and budget constraints on current optical satellites, the acquisition of satellite images with the best resolutions is not practicable. In this article, aiming to produce products with high spectral (HS) and temporal resolutions, we introduced a two-stream spectral–temporal fusion technique based on attention mechanism called STA-Net. STA-Net aims to combine high spectral and low temporal (HSLT) resolution images with low spectral and high temporal (LSHT) resolution images to generate products with the best characteristics. The proposed technique involves two stages. In the first one, two fused images are generated by a two-stream architecture based on residual attention blocks. The temporal difference estimator stream estimates the temporal difference between HS images at desired and neighboring dates. The reflectance difference estimator is the second stream. It predicts the reflectance difference between the input images (HS–LS) to map LS images into HS products. In the second stage, a reconstruction network combines the latter two-stream outputs via an effective learnable weighted-sum strategy. The two-stage model is trained in an end-to-end fashion using an effective loss function to ensure the best fusion quality. To the best of our knowledge, this work represents the first attempt to address the spectral–temporal fusion using an end-to-end deep neural network model. Experimental results conducted on two actual datasets of Sentinel-2 (HSLT:10 spectral bands and long revisit period) and Planetscope (LSHT: four spectral bands and daily images) images, which proved the effectiveness of the proposed technique with respect to baseline technique.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Simulation Framework and Case Studies for the Design of Sea Surface
           Salinity Remote Sensing Missions

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      Authors: Alexander Akins;Shannon Brown;Tong Lee;Sidharth Misra;Simon Yueh;
      Pages: 1321 - 1334
      Abstract: L-band microwave radiometers have now been used to measure sea surface salinity (SSS) from space for over a decade with the SMOS, Aquarius, and SMAP missions, and it is expected that the launch of the CIMR mission in the later half of this decade will ensure measurement continuity in the near future. Beyond these missions, it is useful to consider how future missions can be designed to meet different scientific objectives and performance requirements as well as to fit within different cost spaces. In this article, we present a software simulator for remote sensing measurements of ocean state capable of generating L1- and L2- equivalent data products for an arbitrary spacecraft mission including multifrequency fixed-pointing or scanning microwave radiometers.This simulator is then applied to case studies of SSS measurement over selected areas of interest, including the Gulf Stream, Southern Ocean, and Pacific tropical instability wave regions. These simulations illustrate how different design choices concerning receiver bandwidth and revisit time can improve the detection of SSS features in these regions from the mesoscale to the seasonal scale.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Convolutional Transformer-Based Few-Shot Learning for Cross-Domain
           Hyperspectral Image Classification

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      Authors: Yishu Peng;Yaru Liu;Bing Tu;Yuwen Zhang;
      Pages: 1335 - 1349
      Abstract: In cross-domain hyperspectral image (HSI) classification, the labeled samples of the target domain are very limited, and it is a worthy attention to obtain sufficient class information from the source domain to categorize the target domain classes (both the same and new unseen classes). This article investigates this problem by employing few-shot learning (FSL) in a meta-learning paradigm. However, most existing cross-domain FSL methods extract statistical features based on convolutional neural networks (CNNs), which typically only consider the local spatial information among features, while ignoring the global information. To make up for these shortcomings, this article proposes novel convolutional transformer-based few-shot learning (CTFSL). Specifically, FSL is first performed in the classes of source and target domains simultaneously to build the consistent scenario. Then, a domain aligner is set up to map the source and target domains to the same dimensions. In addition, a convolutional transformer (CT) network is utilized to extract local-global features. Finally, a domain discriminator is executed subsequently that can not only reduce domain shift but also distinguish from which domain a feature originates. Experiments on three widely used hyperspectral image datasets indicate that the proposed CTFSL method is superior to the state-of-the-art cross-domain FSL methods and several typical HSI classification methods in terms of classification accuracy.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Analyzing Gradual Vegetation Changes in the Athabasca Oil Sands Region
           Using Landsat Data

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      Authors: Moritz Lucas;Antara Dasgupta;Björn Waske;
      Pages: 1365 - 1377
      Abstract: Oil sand mining in northern Alberta/Canada in the Athabasca region is a major intrusion into the otherwise pristine natural environment. The various types of oil sands mining, transport, and processing are causing large-scale discharge of pollutants. Accordingly, this study examined the gradual changes in the physically undisturbed vegetation, that occurred from 1984 to 2021 in the Athabasca oil sands monitoring region. First, the abrupt changes were masked out with the help of auxiliary and Landsat data. Subsequently, a normalized burn ratio Landsat time-series was applied to the LandTrendr algorithm on the Google Earth Engine. In order to interpret gradual changes, measurement criteria were used to describe vegetation development, vulnerability, and variability. In addition, the spatial and temporal relationship of these to oil sand opencast mines, processing facilities, and steam assisted gravity drainage (SAGD) mines was examined. The results showed that a major part of the vegetation in the Athabasca oil sand monitoring region underwent a positive development (65.9%). However, around the opencast mines a negative vegetation development and stability within a radius of 10 km could be observed. In the surroundings of processing facilities, the development and stability of vegetation was disturbed within a radius of 2 km. Thereby the analysis of land cover classes showed that deciduous, coniferous, and mixed forest are disproportionately affected. Conversely, no negative influences on neighboring vegetation could be detected around SAGD mines. The temporal analysis showed that vegetation disturbance was most pronounced between 1990 and 2000, but recovered in recent years.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Blockchain-Assisted Verifiable and Secure Remote Sensing Image Retrieval
           in Cloud Environment

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      Authors: Xue Ouyang;Yanyan Xu;Yangsu Mao;Yunqi Liu;Zhiheng Wang;Yuejing Yan;
      Pages: 1378 - 1389
      Abstract: Secure retrieval of remote sensing images in an outsourced cloud environment garners considerable attention. Since the cloud service provider (CSP) is considered as a semitrusted third party that may return incorrect retrieval results to save computational resources or defraud retrieval fees for profit, it becomes a critical challenge to achieve secure and verifiable remote sensing image retrieval. This article presents a secure retrieval and blockchain-assisted verifiable scheme for encrypted remote sensing images in the cloud environment. In response to the characteristic that geographical objects in remote sensing images with clear category attributes, we design a remote sensing image retrieval method to facilitate secure and efficient retrieval. In addition, we propose a verifiable method combined with blockchain and Merkle trees for checking the integrity and correctness of the storage and retrieval services provided by CSP, which can replace the traditional third-party auditor. The security analysis and experimental evaluation demonstrate the security, verifiability, and feasibility of the proposed scheme, achieving secure remote sensing image retrieval while preventing malicious behavior of CSP.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
  • Anomaly Detection of Hyperspectral Images Based on Transformer With
           Spatial–Spectral Dual-Window Mask

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      Authors: Song Xiao;Tian Zhang;Zhangchun Xu;Jiahui Qu;Shaoxiong Hou;Wenqian Dong;
      Pages: 1414 - 1426
      Abstract: Anomaly detection has become one of the crucial tasks in hyperspectral images processing. However, most deep learning-based anomaly detection methods often suffer from the incapability of utilizing spatial–spectral information, which decreases the detection accuracy. To address this problem, we propose a novel hyperspectral anomaly detection method with a spatial–spectral dual-window mask transformer, termed as S2DWMTrans, which can fully extract features from global and local perspectives, and suppress the reconstruction of anomaly targets adaptively. Specifically, the dual-window mask transformer aggregates background information of the entire image from a global perspective to neutralize anomalies, and uses neighboring pixels in a dual-window to suppress anomaly reconstruction. An adaptive-weighted loss function is designed to further suppress anomaly reconstruction adaptively during network training process. According to our investigation, this is the first work to apply transformer to hyperspectral anomaly detection. Comparative experiments and ablation studies demonstrate that the proposed S2DWMTrans achieves competitive performance.
      PubDate: 2023
      Issue No: Vol. 16 (2023)
       
 
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