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  Subjects -> ELECTRONICS (Total: 207 journals)
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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: 66  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1404
Published by IEEE Homepage  [228 journals]
  • Spatio-Temporal Evolution Monitoring and Analysis of Tidal Flats in Beibu
           Gulf From 1987 to 2021 Using Multisource Remote Sensing

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      Authors: Ertao Gao;Guoqing Zhou;Shuxian Li;Bolin Fu;Yunzhi Xiao;Yanping Lan;Feng Wang;Jiasheng Xu;Qiang Zhu;Yuhang Bai;
      Pages: 6099 - 6114
      Abstract: Tidal flats are an important part of coastal wetland systems and play an irreplaceable role in maintaining the health of global coastal ecosystems, resisting natural coastal disasters, and sequestering blue carbon. For the coastal zone around Beibu Gulf, where tidal flats are densely distributed, the spatiotemporal characteristics of tidal flats evolution and its key driving factors remain unclear. This article took the Beibu Gulf as the study area, and constructed an integrated tidal flats extraction algorithm of “maximum spectral index composite (MSIC) + Otsu algorithm (OTSU) + DEM correction” based on the cloud computing platform of Google Earth Engine to realize a large-scope and high-accuracy automatic extraction of tidal flats. The results discovered that 1) the tidal flats extraction method proposed in this study has high accuracy, with the overall accuracy of the confusion matrix reaching 93.9%, and the Kappa coefficient reaching 0.82. The user accuracy and mapping accuracy of the tidal flats are both greater than 85%. 2) In 35 years, the total tidal flats area of the Beibu Gulf has decreased by 683.9 km2, a reduction of 29%, and the average annual rate of change was −19.5 km2/a. 3) The fragmentation state of the tidal flats in Beibu Gulf intensified with time, and the natural driving forces of the tidal flats were mainly the rise in sea level, decrease in the amount of sediment in the river entering the sea, and expansion of mangrove trees. Human-driven forces were mainly coastal economic development and offshore aquaculture.
      PubDate: THU, 09 MAY 2024 09:16:23 -04
      Issue No: Vol. 17, No. null (2024)
       
  • An Advanced Interferometric Baseline Estimation Method (IBEM) for
           Spaceborne Bistatic SAR

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      Authors: Yanyan Zhang;Junfeng Li;Pingping Lu;Robert Wang;
      Pages: 6115 - 6125
      Abstract: Spaceborne bistatic synthetic aperture radar (BiSAR) employs single-pass cross-track interferometry (XTI) to invert digital elevation models (DEMs) of target areas. However, the accuracy of the inverted DEM is affected by various factors, especially interferometric baseline estimation. To this end, this article proposes an advanced interferometric baseline estimation method (IBEM) for spaceborne BiSAR. First, the IBEM compensates for the time synchronization deviation of BiSAR systems using the pulse exchange phase synchronization method. Subsequently, the time, position, and velocity vectors of satellites are input into a high-precision orbit propagator to derive the state vectors at the imaging time. Leveraging these state vectors, the interferometric baseline is calculated with the main satellite that transmits radar signals as the origin of the coordinate system. Eventually, interferometric baseline estimation and DEM generation are performed based on two sets of data from the LuTan-1 (LT-1) BiSAR system, and the generated DEMs are compared with the external reference DEM to evaluate the performance of the IBEM and the LT-1 system. Results indicate that the proposed IBEM can accurately estimate the interferometric baseline and has extensive application prospects in future spaceborne multibaseline Interferometric SAR missions.
      PubDate: FRI, 10 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Inversion of Time Series Elevation Changes in Open Pit Mining Areas Based
           on Adjacent Orbital Sentinel-2 Images

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      Authors: Jianqi Lou;Chaoying Zhao;Guangrong Li;Dongni Li;
      Pages: 6126 - 6133
      Abstract: The orthorectified Sentinel-2 images have systematic offset errors in areas with large surface elevation changes in the cases of open pit mining, land collapse and glacier movement, which can lead to the misinterpretation of geological features classification from one scene image, or bias of deformation results from multiple images. Such systematic offset errors are mainly caused by the outdated digital elevation model (DEM) during the orthorectification processing. In turn, the systematic offsets can be applied to estimate the surface elevation changes. We first deduce the pixel offset of orthorectified Sentinel-2 image caused by outdated DEM based on imaging geometry, new DEM and projection equations. The sum of two offsets by two Sentinel-2 images from adjacent orbits are verified by the results calculated directly by pixel offset tracking (POT) algorithm to the same images. The correlation coefficient R2 between two offsets amounts to 0.95, and the standard deviation of their difference is of 4.04 m. Second, the offsets by POT algorithm between two adjacent orbits are used to derive the time series elevation changes. Finally, the surface elevation changes are validated using the ICESat-2 point cloud and the SRTM DEM. Results show that the standard deviation of height change difference is around 4.13 m. This method is able to reveal the historical surface elevation changes related to the geophysical or anthropogenic process.
      PubDate: THU, 16 MAY 2024 09:15:58 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Rethinking Building Change Detection: Dual-Frequency Learnable Visual
           Encoder With Multiscale Integration Network

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      Authors: Chuan Xu;Haonan Yu;Liye Mei;Ying Wang;Jian Huang;Wenying Du;Shuangtong Jin;Xinliu Li;Minglin Yu;Wei Yang;Xinghua Li;
      Pages: 6174 - 6188
      Abstract: Remote sensing (RS) image change detection (CD) methods based on deep learning, such as convolutional neural networks (CNNs) and transformers, are still spatial domain-based image processing methods by nature, and their detection accuracy is strongly affected by chromatic aberration due to imaging time, shadows caused by lighting conditions, and object confusion and other disturbances. In this study, we revisit CD from a signal processing perspective, framing it as the task of consistency detection of the distributional features of two 2-D signals. We aim to extract the primary components of the two signals while suppressing interfering noises. To address this, we propose a novel CD method called DFNet, which leverages a dual-frequency learnable encoder. First, we construct a dual-frequency feature encoder Siamese framework to capture local high-frequency signals and global low-frequency signals using CNN and attention mechanisms after dividing the input RS image signals into two channels. Second, we introduce the frequency explicit visual center module as a part of the multifrequency-domain dense interaction (MFDDI) module at the decoder stage, allowing long-distance dependency to be established between high–low frequency components in the same layer as well as signal aggregation in regions of small edge variations. In addition, the MFDDI module adopts a layer-by-layer interactive fusion approach to synthesize discriminative information in a wide frequency-domain range, enhancing the characterization capability of frequency-domain signals. We conduct comparison experiments with the current mainstream methods on the land cover dataset SYSU-CD and two building datasets, LEVIR-CD and WHU-CD, and the results show that our method is not only resistant to interference but also outperforms all the comparison methods.
      PubDate: MON, 20 MAY 2024 09:15:55 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Scanning Error Compensation in Ground-Based ArcSAR Monitoring

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      Authors: Yunkai Deng;Hanpu Zhou;Weiming Tian;Xin Xie;Wenyu Li;Cheng Hu;
      Pages: 6215 - 6223
      Abstract: Ground-based arc-scanning synthetic aperture radar (GB-ArcSAR) can perform 360° scanning and has a large field of view. Based on the differential interferometry technique, GB-ArcSAR can be utilized to measure the surface deformation. However, affected by the rotating motion, rescanning angle error and rotation center offset during repeated scanning could occur. Through theoretical analysis, this article proves that the rescanning angle error has nearly no effect on the interferometric phase and can be negligible. The phase error caused by the rotation center offset can be built as a linear multiparameter model based on the multivariate Taylor expansion. Simulations are made to analyze the effect of the rotation center offset. A compensation method based on permanent scatterer technology is proposed, by using the least squares method, which jointly compensates the rotation center offset error and the atmospheric phase disturbance error. The compensation performance of the proposed method is validated in different scenarios, which can effectively improve the accuracy of deformation measurement.
      PubDate: THU, 09 MAY 2024 09:16:23 -04
      Issue No: Vol. 17, No. null (2024)
       
  • First Assessment of SDGSAT-1 TIS Thermal Infrared Bands Calibration Using
           Landsat 9 TIRS-2

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      Authors: Min Zhu;Qiyao Wang;Jianing Yu;Zhuoyue Hu;Fansheng Chen;
      Pages: 6224 - 6232
      Abstract: SDGSAT-1, the inaugural Earth science satellite commissioned by the Chinese Academy of Sciences, successfully lifted off from China's Taiyuan Satellite Launch Center on 5 November 2021. Cross calibration has emerged as an indispensable methodology, providing an effective means for quality assessment, stability monitoring, and uncertainty analysis. This is achieved through the meticulous selection of an appropriate reference instrument. In our investigation, we conducted a comprehensive evaluation of the absolute radiometric calibration accuracy of the thermal infrared spectrometer (TIS), a pivotal component aboard SDGSAT-1. Our evaluation centered on a meticulous comparative analysis, where we juxtaposed the brightness temperature (BT) values recorded by TIS with the BT values derived from TIRS-2, computed following the spectral alignment with TIS. Our findings reveal that the current absolute BT bias for the B2 channel of TIS is less than 0.52 K. Meanwhile, the B3 channel exhibits a slightly larger fluctuation in absolute BT bias, yet remains within acceptable parameters, registering at less than 1 K. Notably, B3 emerges as the more dependable option for accurately gauging the temperature of the target region. Specifically, when the BT of B3 surpasses 290 K, the BT bias remains consistently below 0.3 K. In light of the approximately 40-min time difference in the water surface data utilized in our study, we conducted simulations to assess the channel BT of TIS under the scenario of a 0.25 °C water temperature variation. Our simulations conclusively demonstrate that the corresponding change in BT does not exceed 0.25 K.
      PubDate: MON, 13 MAY 2024 09:16:12 -04
      Issue No: Vol. 17, No. null (2024)
       
  • An Intelligent System for Outfall Detection in UAV Images Using
           Lightweight Convolutional Vision Transformer Network

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      Authors: Mingxin Yu;Ji Zhang;Lianqing Zhu;Shengjun Liang;Wenshuai Lu;Xinglong Ji;
      Pages: 6265 - 6277
      Abstract: Unmanned aerial vehicle aerial photography technology has become a crucial tool for detecting outfalls that discharge into rivers and oceans. However, the current retrieval process in aerial images relies heavily on visual interpretation by skilled experts, which is time-consuming and inefficient. To address this issue, we propose a lightweight deep-learning model for detecting outfall objects in aerial images. Specifically, the backbone of our proposed model is a lightweight convolutional vision transformer network, which consists of two novel blocks: separated downsampled self-attention and convolutional feedforward network with a shortcut. These blocks are designed to capture information at different granularities in the feature map and build both local and global representations. The model utilizes a path aggregation feature pyramid network as the neck and a lightweight decoupled network as the head. The experiments demonstrate that our model achieves the highest accuracy of 81.5% while utilizing only 2.47 M parameters and 3.95 GFLOPs. Visualization analysis shows that our model pays more attention to true outfall objects. Additionally, we have developed an intelligent outfall detection system based on the proposed model, and the experimental results show that it performs well in the task of outfall detection.
      PubDate: TUE, 14 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
  • A Learning Framework With Multispectral Band-Differentiated Encoding for
           Remote Sensing Water Body Detection

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      Authors: Debin Wei;Hongji Xie;Pinru Li;Yongqiang Xu;
      Pages: 6278 - 6289
      Abstract: Classic deep convolutional neural network (DCNN) models have demonstrated notable efficacy in segmenting remote sensing images. However, their ability to enhance the precision of water body detection, particularly for smaller ones amid intricate backgrounds, remains challenging. This article proposes the negative Laplacian filter (NLF) method as a solution, enhancing regional color contrast during preprocessing to capture more intricate details effectively. Furthermore, a novel approach employs a differential dual-encoding structure that encodes diverse spectra based on their spectral attributes. Lastly, leveraging prior insights from remote sensing, we introduce the weak label weight adjustment operation for refining predicted images in postprocessing stages. The proposed model significantly outperforms the comparison models on our remote sensing water body dataset.
      PubDate: FRI, 10 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Urban Trees Expansion and Exposure Improvement in China: Insights From
           Satellite Observations (1985–2023)

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      Authors: Tao Liu;Xiaoping Liu;Yaotong Cai;
      Pages: 9694 - 9705
      Abstract: Urban tree cover plays a pivotal role in bolstering the sustainability, livability, and resilience of cities. However, the changes in urban tree cover and exposure under the backdrop of urbanization in China remain unclear. This study investigates the spatial distribution and temporal trends of urban tree cover expansion and exposure change from 1985 to 2023. Our findings highlight significant progress in enhancing urban tree cover, with the average tree cover in Chinese urban areas reaching 11.9% in 2023, representing a remarkable 116% increase from 5.5% in 1985. However, this positive trend contrasts with persistent deficiencies in forest resources, as reflected by the fact that urban forest cover in Chinese cities was only 4.1% in 2023, compared with 0.1% in 1985. Furthermore, our study revealed substantial heterogeneity in the spatiotemporal variations of urban tree cover. Only five cities exhibited a decreasing trend in tree cover during the study period, with coastal cities demonstrating significantly higher tree cover compared with inland cities. Additionally, we observed that urban tree cover and expansion rates decreased significantly with increasing latitude. Moreover, nationwide, there has been a sustained and accelerated improvement in urban tree exposure levels, with average tree exposure increasing from 0.66% in 1985 to 7.45% in 2020. The 69% of cities experienced positive changes in tree exposure levels, while only 1% exhibited negative changes. These findings offer valuable insights for policymakers, planners, and stakeholders to foster greener, more resilient, and sustainable urban environments in China.
      PubDate: TUE, 07 MAY 2024 09:16:17 -04
      Issue No: Vol. 17, No. null (2024)
       
  • ABBD: Accumulated Band-Wise Binary Distancing for Unsupervised
           Parameter-Free Hyperspectral Change Detection

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      Authors: Yinhe Li;Jinchang Ren;Yijun Yan;Ping Ma;Maher Assaad;Zhi Gao;
      Pages: 9880 - 9893
      Abstract: As a fundamental task in remote sensing earth observation, hyperspectral change detection (HCD) aims to identify the changed pixels in bitemporal hyperspectral images. However, the water-absorption effect, poor weather conditions, noise and inconsistent illumination as well as lack of accurate ground truth has made HCD particularly challenging. To tackle these challenges, a novel Accumulated Band-wise Binary Distancing (ABBD) model was proposed for unsupervised parameter-free HCD. Rather than relying on the absolute pixel difference with thresholding in conventional approaches, the binary distancing only indicated whether a pixel was changed or not in a certain band, which could alleviate the adverse effects of noise-induced inconsistency of measurement. The band-wise binary distance map is then accumulated to form a grayscale change map, on which the simple k-means was applied for a final binary decision-making. Experiments on three publicly available datasets have validated the superiority of our approach, which has yielded comparable or slightly better results in comparison to a few state-of-the-art methods including several deep learning models.
      PubDate: THU, 30 MAY 2024 09:16:19 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Space Target 3-D Reconstruction Using Votes Accumulation Method of ISAR
           Image Sequence

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      Authors: Dan Xu;Xuan Wang;Zhixin Wu;Jixiang Fu;Yuhong Zhang;Jianlai Chen;Mengdao Xing;
      Pages: 9881 - 9893
      Abstract: Combining inverse projection and vote accumulation methods, we propose a novel 3-D geometric reconstruction method based on inverse synthetic aperture radar image sequence for triaxially stabilized space targets. Considering that the translation of a triaxially stabilized space target results in a large angular rotation of the radar line-of-sight and an attitude adjustment rotation of the target itself, the projection relationship between the radar imaging plane and a triaxially stabilized space target is constructed first. Subsequently, we perform an inverse projection on the dominant points extracted from each imaging plane to determine the location of the 3-D candidate scatterers and assign a voting value of 1 to each position. By accumulating these vote values, we can obtain the total number of votes for the candidate scatterer position. We treat candidate 3-D scatterers with a large cumulative number of votes as actual scatterers. Thus, the target 3-D geometry can be obtained. Compared with the traditional matrix factorization-based 3-D reconstruction methods, this method is more accurate and ensures that the target pose is consistent with the actual pose. At the same time, compared with 3-D reconstruction methods based on the energy accumulation ideas, this method produces fewer false scatterers and has higher credibility. The experimental results based on the simulated point target and electromagnetic data are presented to validate the effectiveness and robustness of the proposed method.
      PubDate: MON, 20 MAY 2024 09:15:55 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Potential of Sample Migration and Explainable Machine Learning Model for
           Monitoring Spatiotemporal Changes of Wetland Plant Communities

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      Authors: Kaidong Feng;Dehua Mao;Jianing Zhen;Haiguang Pu;Hengqi Yan;Ming Wang;Duanrui Wang;Hengxing Xiang;Yongxing Ren;Ling Luo;Zongming Wang;
      Pages: 9894 - 9906
      Abstract: The composition and dynamics of wetland plant communities play a critical role in maintaining the functionality of wetland ecosystems and serve as important indicators of wetland degradation and restoration. Accurately identifying wetland plant communities using remote sensing techniques remains challenging due to the complex environment and cloud contamination. Here, we applied a sample migration method based on change vector analysis and a random forest (RF) classifier incorporating SHapley Additive exPlanations (SHAP) to explore the spatiotemporal changes of wetland plant communities in the western Songnen Plain of China between 2016 and 2022, and to better understand the decision logic of the RF model. Our work achieved accurate annual wetland classification at the community scale, with an average overall accuracy of 89.5% and an average kappa coefficient of 0.87. Our analysis revealed different spatiotemporal change characteristics of wetland plant communities in the western Songnen Plain and three national nature reserves. The SHAP model showed that MOS_IRECI is the most important feature determining the prediction results of the RF model, and the importance of the features differs at global and local levels. This study confirms the feasibility of annual dynamic monitoring of wetland plant communities at a regional scale. The results are expected to provide a reference for the fine and sustainable management of wetland resources in the western Songnen Plain, as well as valuable data support for related wetland ecology research.
      PubDate: MON, 13 MAY 2024 09:16:12 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Spectral Quadratic Variation Regularized Autoweighted Tensor Ring
           Decomposition for Hyperspectral Image Reconstruction

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      Authors: Xinwei Wan;Dan Li;Fanqiang Kong;Yanyan Lv;Qiang Wang;
      Pages: 9907 - 9921
      Abstract: The structure information of hyperspectral image (HSI) is well-characterized by tensors, surpassing the capabilities of traditional compressive sensing reconstruction models based on vectors and matrices. Tensor decomposition has been integrated with other regularizations in the model-based reconstruction algorithms to capture more priors. However, the existing tensor decomposition fails to achieve the best low-rank approximation. The effectiveness of model-based reconstruction methods can be promoted. In this article, a subspace-based model utilizing spectral quadratic variation regularized autoweighted tensor ring (TR) decomposition is proposed to explore the multiple-layer spatial–spectral priors of HSI. The original HSI is decomposed into the feature image and spectral basis to explore the first-layer spectral low-rankness. To capture the second-layer low-rank prior, TR decomposition is applied to obtain the effective low-rankness approximation and low computational complexity. The tensor nuclear norm is employed to describe underlying structure priors of the TR factors, which address the deficiency in tensor rank robustness and enhance the reconstruction quality. An autoweighted mechanism is utilized to account for the varying contributions of different TR factors to the low-rank approximation. Moreover, embedding spectral quadratic variation into subspace decomposition enhances spectral smoothness and continuity. Alternating minimization is used to optimize the spectral basis and feature HSI. Through comparative experiments on three datasets, the superiority of the proposed model is demonstrated.
      PubDate: MON, 20 MAY 2024 09:15:55 -04
      Issue No: Vol. 17, No. null (2024)
       
  • SGNet: A Transformer-Based Semantic-Guided Network for Building Change
           Detection

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      Authors: Jiangfan Feng;Xinyu Yang;Zhujun Gu;
      Pages: 9922 - 9935
      Abstract: Building change detection (BCD) is a widely used method for monitoring human activities. Despite advancements in deep learning (DL) in computer vision, recent DL-based BCD methods still face challenges in extracting discriminative features due to irrelevant noisy changes and the lack of consideration for specific semantic information related to changes. To address this limitation, we propose the semantic-guided network (SGNet), a multiscale pure Transformer-based BCD method that delves into more discriminative class-specific feature representations for changed buildings. Specifically, we first breakdown the change detection process into multiple stages: changed semantics extraction, enhancement, fusion, and decoding. Then, we analyze the nature of building change events in remote sensing images (RSIs) and categorize them into three main groups. To detect positive changes and reduce noise, we developed the various changes extraction module. In addition, we introduce the semantic-guided changed-semantics augmentation module to enhance the semantic information of target changes while suppressing irrelevant regions or objects. We also propose the various changes fusion module to model semantic latency in bitemporal RSIs. Extensive experiments on two building change datasets, i.e., LEVIR-CD and WHU-CD demonstrate that our SGNet(MiT-b0) outperforms the state-of-the-art baselines and achieves F1 score of 90.99% and 92.47%, respectively, with improvements of 1.9% and 7.89%, respectively, highlighting the exceptional sample-efficient learning capability.
      PubDate: FRI, 17 MAY 2024 09:15:47 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Ship Detection From Raw SAR Echoes Using Convolutional Neural Networks

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      Authors: Kevin De Sousa;Georgios Pilikos;Mario Azcueta;Nicolas Floury;
      Pages: 9936 - 9944
      Abstract: Synthetic aperture radar (SAR) is an indispensable tool for marine monitoring. Conventional data processing involves data down-linking and on-ground operations for image focusing, analysis, and ship detection. These steps take significant amount of time, resulting in potentially critical delays. In this work, we propose a ship detection algorithm that operates directly on raw SAR echoes, based on convolutional neural networks. To evaluate our approach, we performed experiments using raw data simulations and real raw SAR data from Sentinel-1 stripmap mode scenes. Preliminary results on this set show the capability of detecting multiple ships from raw data with similar accuracy as using single-look-complex images as input. Simultaneously, running time is reduced significantly, by-passing the image focusing step. This illustrates the great potential of deep learning, moving toward more intelligent SAR systems.
      PubDate: THU, 09 MAY 2024 09:16:23 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Urban SAR Tomography With Z-Structure Constraint of Buildings

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      Authors: Rui Guo;Yuxin Gao;Zishuai Ren;Zhao Zhang;Jinwei Xie;Bei Yang;
      Pages: 9945 - 9960
      Abstract: Synthetic aperture radar tomography (TomoSAR) technology effectively mitigates issues such as severe layover in high-resolution synthetic aperture radar (SAR) urban imagery, presenting numerous applications in reconstructing complex 3-D urban scenes. However, prevailing TomoSAR methodologies usually overlook the geometrical feature of the targets, which are particularly pronounced in buildings in urban scenes. To improve the quality of 3-D reconstruction of building targets for large-scale urban scenes, we propose geometric-guided TomoSAR processing in this article. First, the geometrical feature referred to a Z-structure of buildings in TomoSAR point cloud is studied. Then, a Z-structure information extraction approach is proposed to provide prior information for subsequent geometric constraints. Finally, the Z-structure-constraint-based tomographic algorithm is proposed, optimizing the solution space of the original algorithms. Experiments are conducted on real SAR data, and 3-D point clouds of the entire urban scenes are obtained. The proposed algorithm demonstrates commendable performance in point cloud entropy and concentration, especially in the inversion of different floor structures.
      PubDate: FRI, 10 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Multiscale and Multidirection Feature Extraction Network for Hyperspectral
           and LiDAR Classification

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      Authors: Yi Liu;Zhen Ye;Yongqiang Xi;Huan Liu;Wei Li;Lin Bai;
      Pages: 9961 - 9973
      Abstract: Deep learning (DL) plays an increasingly important role in Earth observation by multisource remote sensing. However, the current DL-based methods do not make a fully use of the complementary information among multisource remote sensing data, such as hyperspectral image and light detection and ranging data, and lack the consideration of multiscale, directional, and fine-grained features. To address these issues, a multiscale and multidirection feature extraction network is proposed in this article. Specifically, the multiscale spatial feature (MSSpaF) module is designed to extract the MSSpaFs, and then, these features are fused by feature concatenation operation. In addition, the multidirection spatial feature module is designed to further extract multidirection and frequency information, employing cross-layer connection and multiscale feature fusion strategy to improve the fineness of the proposed network. Moreover, the spectral feature module is employed to provide detailed spectral information for enhancing the expression ability of multiscale features. Experimental results on three different datasets demonstrate the superior classification performance of the proposed framework.
      PubDate: TUE, 14 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
  • A Novel Multiscale Contrastive Learning Network for Fine-Grained Ocean
           Ship Classification

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      Authors: Shaokang Dong;Jiangfan Feng;Dongxu Fang;
      Pages: 9989 - 10005
      Abstract: Fine-grained ocean ship classification plays a crucial role in maritime military surveillance, traffic management, and antismuggling operations. However, the complex backgrounds of remote sensing images (RSIs), as well as significant interclass similarities and intraclass differences, result in poor classification performance. Hence, we propose MSCL-Net, a multiscale contrastive learning network for fine-grained ship classification (FGSC). First, we introduce ResNet50 as the backbone network and extract the multilayer features by using the FPN for FGSC. Second, a channel spatial attention module (CSAM) is proposed to extract the similarity (contrastive) feature of the same class, strengthening the representation learning ability for addressing issues caused by significant interclass similarity and intraclass difference. Third, a region cropping and enlargement module is proposed to extract the fine-grained features of local discriminant regions in RSIs to overcome the challenge of background complexity. Finally, we used the CSAM to fuse the features of the original image and the cropped region image for FGSC. In addition, we introduce a combined loss based on center loss and PolyLoss to enhance the discrimination ability of features and make it more suitable for the imbalance dataset compared with cross-entropy. We use a public FGSC dataset, FGSC-23, and our FGSC-41 to evaluate the performance of MSCL-Net. The experimental results show superior performance compared to other state-of-the-art methods, highlighting the effectiveness of MSCL-Net in addressing the challenges associated with FGSC. Ablation experiments also suggest the effectiveness of our design.
      PubDate: FRI, 10 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Detection of Flooded Areas Caused by Typhoon Hagibis by Applying a
           Learning-Based Method Using Sentinel-1 Data

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      Authors: Takahiro Igarashi;Hiroyuki Wakabayashi;
      Pages: 10006 - 10012
      Abstract: Typhoon Hagibis (No. 19, in Japan) made landfall in Koriyama City in Fukushima Prefecture, Japan, on October 13, 2019. The consequent floods damaged built-up areas in the city center. Furthermore, rice production was affected because the flood occurred before rice harvesting. Although the effects of inundation using Sentinel-1 synthetic aperture radar (SAR) data have been studied, further quantitative analyses are necessary to detect flooded areas using SAR data because the changes in the backscattering coefficient are complex and vary between built-up and paddy areas. Here, we aimed to apply a learning-based method to detect flood-damaged areas in both built-up areas and paddy fields. The training and test datasets were derived from variations in backscattering coefficients measured by Sentinel-1 SAR before and during the flooding event. Moreover, changes in SAR data in built-up areas and paddy fields, where flood damage occurred, were used as training data. A support vector machine was applied as a classifier to detect areas damaged by floods. The proposed method can detect flood-damaged areas caused by Typhoon Hagibis in both the built-up and paddy areas. Changing both the backscattering coefficient and texture (entropy) information improved the flood detection accuracy by a kappa coefficient of 0.15 when compared with that achieved using backscattering-only input. Furthermore, upon comparing F-values across categories using dual and single polarization, we found that VV (transmit V and receive V polarizations) enhanced the accuracy of detecting flooded built-up areas, while VH (transmit V and receive H polarizations) yielded improvements in identifying flooded paddy areas.
      PubDate: MON, 13 MAY 2024 09:16:12 -04
      Issue No: Vol. 17, No. null (2024)
       
  • MOBS-TD: Multiobjective Band Selection With Ideal Solution Optimization
           Strategy for Hyperspectral Target Detection

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      Authors: Xudong Sun;Peng Lin;Xiaodi Shang;Huijuan Pang;Xianping Fu;
      Pages: 10032 - 10050
      Abstract: Band selection (BS) is a crucial concept within the realm of remote sensing, involving the selection of the most suitable bands to accurately capture features of landforms and surfaces. Despite the promising results achieved by many existing methods, certain limitations remain. First, most methods rely on a single criterion for band evaluation, leading to an incomplete assessment and limited generalizability of bands. Second, there is a lack of emphasis on target detection; thus, some BS techniques commonly used for classification are less effective for detection. Therefore, this article proposes MOBS-TD, a multiobjective optimization (MO) based BS method specifically designed for target detection, which aims to select bands with better target separation and stronger robustness across various application scenes. Initially, we develop an MO model with three objectives and introduce a novel metric to quantify the target–background separability of bands. Subsequently, a weighted similarity to ideal solution strategy is developed to clearly describe the dominance relations and strike a balance among multiple objectives in evolution. In addition, we devise an evaluation mechanism based on the ratio of maximum to submaximum, which is devised for selecting the optimal solution from the Pareto front, which has been empirically validated to be effective in reducing false alarms. Extensive experiments on real-world datasets demonstrate the competitiveness of MOBS-TD in remote sensing applications.
      PubDate: FRI, 17 MAY 2024 09:15:47 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Cross-Domain Urban Land Use Classification via Scenewise Unsupervised
           Multisource Domain Adaptation With Transformer

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      Authors: Mengmeng Li;Congcong Zhang;Wufan Zhao;Wen Zhou;
      Pages: 10051 - 10066
      Abstract: Current land use classification models based on very high-resolution (VHR) remote sensing images often suffer from high sample dependence and poor transferability. To address these challenges, we propose an unsupervised multisource domain adaptation framework for cross-domain land use classification that eliminates the need for repeatedly using source domain data. Our method uses the Swin Transformer as the backbone of the source domain model to extract features from multiple source domain samples. The model is trained on source domain samples, and unlabeled target domain samples are then used for target domain model training. To minimize the feature discrepancies between the source and target domains, we use a weighted information maximization loss and self-supervised pseudolabels to alleviate cross-domain classification noise. We conducted experiments on four public scene datasets and four new land use scene datasets created from different VHR images in four Chinese cities. Results show that our method outperformed three existing single-source cross-domain methods (i.e., DANN, DeepCORAL, and DSAN) and four multisource cross-domain methods (i.e., M3SDA, PTMDA, MFSAN, and SHOT), achieving the highest average classification accuracy and strong stability. We conclude that our method has high potential for practical applications in cross-domain land use classification using VHR images.
      PubDate: MON, 13 MAY 2024 09:16:12 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Characterizing Fire-Induced Forest Structure and Aboveground Biomass
           Changes in Boreal Forests Using Multitemporal Lidar and Landsat

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      Authors: Tuo Feng;Laura Duncanson;Steven Hancock;Paul Montesano;Sergii Skakun;Michael A. Wulder;Joanne C. White;David Minor;Tatiana Loboda;
      Pages: 10108 - 10125
      Abstract: Wildfire is the dominant stand-replacing disturbance regime in Canadian boreal forests. An accurate quantification of postfire changes in forest structure and aboveground biomass density (AGBD) provides a means to understand the magnitudes of ecosystem changes through wildfires and related linkages with global climate. While multispectral remote sensing has been extensively utilized for burn severity assessment, its capacity for postfire forest structure and AGBD change monitoring has been more limited to date. This study evaluates the interactions among burn severity, forest structure, and fire-return intervals for two representative sites in the western Canadian boreal forest. We adopted burn severity measurements from Landsat to characterize the heterogeneity of wildfire effects, while vertical forest structure information from Lidar was utilized to inform on realized forest changes and carbon fluxes associated with fire. Dominant trees in biomass-rich stands showed higher tolerance to low- and moderate-severity wildfires, while understory vegetation in these same stands showed a severity-invariant response to wildfires indicated by high vegetation mortality regardless of burn severity levels. Compared to a site without previous burn, canopy height and AGBD experienced lower magnitudes of change after subsequent wildfires, explained by a negative feedback between high frequency wildfires and biomass loss ($\overline {\Delta \mathbf{Canopy}\ \mathbf{Height}} $single wildfire = 3.03 m; $\overline {\Delta \mathbf{Canopy}\ \mathbf{Height}} $successive wildfire = 2.47 m; $\overline {\Delta \mathbf{AGBD}} $single wildfire = 8.40 Mg/ha; $\overline {\Delta \mathbf{AGBD}} $successive wildfire = 6.69 Mg/ha). This study provides new insights into forest recovery dynamics following fire disturbance, which is particularly relevant given increased fire frequency and intensity in boreal ecosystems resulting from climate change.
      PubDate: MON, 13 MAY 2024 09:16:12 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Sea Surface Current Retrieval From Sequential SAR and Ocean Color Images
           

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      Authors: Xue Yang;Jinsong Chong;Yawei Zhao;
      Pages: 10126 - 10136
      Abstract: Sea surface currents (SSCs) are of great importance in monitoring the evolving dynamics of oceans and can be effectively obtained from sequential remote sensing images. However, the absence of traceable feature patterns in sequential remote sensing images constrains the capabilities for retrieving SSCs. In this article, an approach was presented to retrieve SSCs by merging currents derived from synthetic aperture radar (SAR) and ocean color (OC) images computed using the maximum cross-correlation (MCC) method. For different OC product performances in retrieving the surface currents, an evaluation criterion was used to select the optimal OC-derived currents before merging. The complete current field was obtained by merging SAR and optimal OC-derived currents based on the correlation coefficient weighted model. Statistical comparisons were performed between the merged and geostrophic currents obtained from the altimetry over the northern Tyrrhenian Sea. The two current fields were in good agreement, with a complex correlation coefficient of 0.65 in magnitude and 2.23° in phase. The oceanic eddy kinematic parameters were successfully calculated based on the complete current field obtained and used to analyze spatio-temporal evolution characteristics.
      PubDate: FRI, 17 MAY 2024 09:15:47 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Mean-Weighted Collaborative Representation-Based Spatial-Spectral Joint
           Classification for Hyperspectral Images

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      Authors: Hongjun Su;Dezhong Shi;Zhaohui Xue;Qian Du;
      Pages: 10158 - 10173
      Abstract: Collaborative representation (CR) models have been widely used in hyperspectral image (HSI) classification tasks. However, most CR classification models lack stability and generalization when targeting small samples as well as spatial homogeneity and heterogeneity problems. Therefore, this article proposes a mean-weighted CR classification model (MWCRC) based on the joint spatial-spectral data. It imposes mean and weighted constraints on the representation coefficients based on CR, which attenuates the noise effect and increases the distinguishability between classes. Second, a sample augmentation method based on the principle of minimizing the representation residuals is proposed. Sample augmentation is realized through initial classification and calculation of representation residuals to achieve the objective of consolidating model stability and improving classification accuracy. Meanwhile, in order to alleviate the problem of spatial homogeneity and heterogeneity, the extended morphological profile (EMP) and the stacking approach are utilized to construct the joint spatial-spectral data for the classification of MWCRC. The superiority of the proposed method is demonstrated by experimental validation using a small number of training samples in three real datasets.
      PubDate: TUE, 07 MAY 2024 09:16:17 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Generating Adversarial Examples Against Remote Sensing Scene
           Classification via Feature Approximation

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      Authors: Rui Zhu;Shiping Ma;Jiawei Lian;Linyuan He;Shaohui Mei;
      Pages: 10174 - 10187
      Abstract: The existence of adversarial examples highlights the vulnerability of deep neural networks, which can change the recognition results by adding well-designed perturbations to the original image. It brings a great challenge to the remote sensing images (RSI) scene classification. RSI scene classification primarily relies on the spatial and texture feature information of images, making attacks in the feature domain more effective. In this study, we introduce the feature approximation (FA) strategy, which generates adversarial examples by approximating clean image features to virtual images that are designed to not belong to any category. Our research aims to attack image classification models that are trained with RSI and discover the common vulnerabilities of these models. Specifically, we benchmark the FA attack using both featureless images and images generated via data augmentation methods. We then extend the FA attack to multimodel FA (MFA), improving the transferability of the attack. Finally, we show that the FA strategy is also effective for targeted attacks by approximating the input clean image features to the target category image features. Extensive experiments on the remote sensing classification datasets UC Merced and AID demonstrate the effectiveness of the methods in this article. The FA attack exhibits remarkable attack performance. Furthermore, the proposed MFA attack outperforms the success rate achieved by existing advanced targetless black-box attacks by an average of over 15%. The FA attack also performs better compared to multiple existing targeted white-box attacks.
      PubDate: MON, 13 MAY 2024 09:16:11 -04
      Issue No: Vol. 17, No. null (2024)
       
  • A Robust Space Target Extraction Algorithm Based on Standardized
           Correlation Space Construction

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      Authors: Han Wang;Siyang Chen;Zhihua Shen;Kunpeng Wang;Meiya Duan;Wenbo Yang;Bin Lin;Xiaohu Zhang;
      Pages: 10188 - 10202
      Abstract: With the increasing amount of space debris in near-earth space, the surveillance of space has become more crucial to prevent potential space collisions. In an optical surveillance system, a multitude of debris with varying sizes, speeds, and intensities exists within images, which brings great difficulties for the extraction of targets. Current methodologies mainly emphasize local saliency enhancement and source extraction based on global statistical attributes, yet often overlook the distribution similarity of individuals and how to map them in a standardized space. To address this limitation, this article introduces an innovative transformation from the grayscale space to the correlation space. Initially, a fourfold-structure model is designed to measure the local correlation for each element in the grayscale space. Subsequently, the standardized correlation space is constructed by correlation measurement. Then, combining an adaptive threshold choosing method based on statistics and a threshold limitation strategy based on correlation, the individuals can be separated from the feature space. Finally, a deblending method is applied to disentangle merged individuals based on the local gradient correlation. Thoroughly assessed using ten subimage sequences featuring targets with typical traits against complex backgrounds, the results confirm its superior extraction capabilities and effectiveness when compared to ten common baseline methods, which achieves an average 90.9% true positive rate in just 2.8 ms. Furthermore, the extraction outcomes serve as a basis for detecting true target trajectories in continuous sequences. This integration holds significant implications for practical applications in space surveillance engineering.
      PubDate: TUE, 07 MAY 2024 09:16:17 -04
      Issue No: Vol. 17, No. null (2024)
       
  • PolSAR Image Classification Framework With POA Align and Cyclic Channel
           Attention

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      Authors: Xiaoxiao Fang;Chu He;Qingyi Zhang;Ming Tong;
      Pages: 10203 - 10220
      Abstract: The sensitivity and dependence of polarization and scattering on target orientation and radar incident angle pose a significant challenge in interpreting polarimetric synthetic aperture radar (PolSAR) images. Previous studies have emphasized the potential advantages of compensating for target orientation and acquiring diverse scattering information by rotating the polarimetric matrix. To fully exploit interpixel polarization information and achieve consistency in the targets' polarization orientation angle (POA), we propose a transformer-based framework that combines POA align with cyclic channel attention for PolSAR image classification. First, we select relevant features and introduce an implicit layer to facilitate unsupervised learning of POA, guiding the alignment of target orientation. Second, considering the inherent correlation between input POA channels and the necessity for more rational and efficient utilization of channel information, we devise a lightweight channel attention module with local cross-channel interaction and a cyclic padding strategy. Finally, this article is devoted to presenting a unified global modeling framework based on transformers. The primary objective is to overcome the limitations of local modeling capability in CNNs and comprehensively capture distinctions between polarization channels and intricate relationships among polarimetric information across pixels. Extensive experiments and analyses have demonstrated the robustness and effectiveness of the proposed method.
      PubDate: MON, 13 MAY 2024 09:16:11 -04
      Issue No: Vol. 17, No. null (2024)
       
  • A Mismatch Removal Method Based on Global Constraint and Local Geometry
           Preservation for Lunar Orbiter Images

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      Authors: Dayong Liu;Zhen Ye;Yusheng Xu;Rong Huang;Lin Xue;Hao Chen;Genyi Wan;Huan Xie;Xiaohua Tong;
      Pages: 10221 - 10236
      Abstract: Mismatch removal is a crucial step in multiview matching of lunar orbiter images. This process involves complex challenges like terrain-induced distortion and nonunique geometric structures due to repetitive textures. Traditional methods, whether global constraints or local constraints, fall short in adequately addressing these issues in orbiter imagery. Therefore, this article proposes an effective method for mismatch removal of orbiter images based on global constraint and local geometry preservation combined with the imaging model. In this method, a clean neighborhood of each matching point based on the characteristic of centralized distribution of the back-projection residuals globally is constructed. In the local region, we define a local minimum geometric polygon consisting of the center feature point and its three neighbors, and combine the similarity of the back-projection difference vectors with the affine invariance of the polygon to distinguish the correct matches and mismatches by measuring the degree of local geometry preservation. A series of experiments encompassing parameter sensitivity analysis, comparison studies and ablation experiments were conducted on the lunar reconnaissance orbiter image datasets to demonstrate the effectiveness and reliability of the proposed method. The results indicate that our method exhibits a notable insensitivity to parameter variations, and outperforms other advanced methods in both qualitative and quantitative evaluations. Moreover, a large-scale orbiter images multiview matching tie points extraction framework is extended based on the proposed mismatch removal method, which can achieve better results than commonly used photogrammetric software in terms of the number and accuracy of tie points.
      PubDate: MON, 13 MAY 2024 09:16:12 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Improvement of Sea Ice Drift Extraction Based on Feature Tracking from
           C-SAR/01 Imagery

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      Authors: Yanli Yang;Tao Xie;Chengzhi Sun;Chao Wang;Jian Li;Xuehong Zhang;
      Pages: 10237 - 10251
      Abstract: In this study, the extraction of sea ice drift from imagery captured by the 1-meter C-SAR 01 satellite (C-SAR/01) was facilitated utilizing the oriented fast and rotated brief algorithm within the feature tracking procedure, thus addressing the previously unexplored area of sea ice drift extraction using C-SAR/01 imagery. The retained keypoints and nearest neighbor distance ratio test for sea ice drift extracted from C-SAR/01 imagery were compared, indicating high reliability with 300 000 and 0.75, respectively. In addition, the local outlier factor algorithm is proposed in this article, which can effectively remove erroneous sea ice drift vectors. The sea ice drift extracted from C-SAR/01 was validated against manually extracted sea ice drift, revealing an uncertainty of 0.271 cm/s in speed and 8.331° in direction. Furthermore, the sea ice drift obtained from the algorithm in this study, when compared with sea ice drift from IABP buoys, exhibits high accuracy, reflecting the robustness of the algorithm.
      PubDate: WED, 22 MAY 2024 09:16:24 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Polarimetric SAR Interferometry Forest Height Inversion Error Model: The
           Impact of the Nonideal System Parameters

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      Authors: Zenghui Huang;Xiaolei Lv;Xiaoshuai Li;
      Pages: 10252 - 10265
      Abstract: The model-based polarimetric interferometric synthetic aperture radar forest height inversion is inherently affected by nonideal system parameters, including channel imbalance, crosstalk, and noise. To investigate the impact of nonideal system parameters on the estimated forest height, this article introduces an analytical forest height inversion error model based on the two-layer randomly oriented volume over ground model. First, the error transfer function is derived to present the forest height error caused by the disturbance of the volume coherence. Then, the coupled effects of the nonideal system parameters on the volume coherence are established and demonstrated to reduce the amplitude of the volume coherence. The effects of channel imbalance and crosstalk are explicitly derived and found to be coupled together to amplify the noise. Finally, the error model is established by combining the error transfer function and the disturbance of the volume coherence caused by the nonideal system parameters. The proposed error model is verified by simulation analyses on real airborne repeat-pass BioSAR 2008 datasets. The results demonstrate that the proposed model can accurately capture the relationship between the height estimation errors and the system parameters.
      PubDate: WED, 08 MAY 2024 09:16:17 -04
      Issue No: Vol. 17, No. null (2024)
       
  • LFHNet: Lightweight Full-Scale Hybrid Network for Remote Sensing Change
           Detection

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      Authors: Xintao Jiang;Shubin Zhang;Jun Gan;Jujie Wei;Qingli Luo;
      Pages: 10266 - 10278
      Abstract: The deep learning-based change detection (CD) methods have achieved remarkable progress with remote sensing imagery. These methods mainly rely on complex feature extraction structures and numerous attention mechanisms to realize effective feature extraction and recognition. However, this results in a significant increase in the number of parameters and the training cost of the whole network. The increase in parameterization can also lead to the degradation of network performance when the amount of training data is insufficient. Thus, it is still promising and challenging to perform reliable CD results through light network design. In this article, we propose a lightweight full-scale hybrid network. The network is comprised of a convolutional neural network (CNN), multilayer perceptron (MLP), and transformer, and it is capable of achieving high performance in CD tasks with a lightweight structure. First, the MLP structures are integrated into the basic network to extract global feature information, compensating for the information loss caused by the convolutional operations of CNN. Second, a full-scale difference module is designed to sufficiently extract the feature information and ensure enough feedforward information. Third, a lightweight transformer is appended at the end of the network to accomplish the spatial-temporal correlation of features, which effectively enhances the quality of the final extracted features. Experimental results on three classical CD datasets show that the proposed method outperforms the state-of-the-art methods.
      PubDate: MON, 13 MAY 2024 09:16:10 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Mesoscale Eddy Detection and Classification From Sea Surface Temperature
           Maps With Deep Neural Networks

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      Authors: Mohammad Mahdi Safari;Alireza Sharifi;Javad Mahmoodi;Dariush Abbasi-Moghadam;
      Pages: 10279 - 10290
      Abstract: Oceanic eddies are a widespread and important occurrence that plays a vital role in the movement of chemicals and energy within the marine ecosystem. Hence, the astute and precise recognition of these swirling currents may greatly contribute to the progress of our comprehension of oceanography. Due to the continuous breakthroughs in state-of-the-art deep learning technology, the population is witnessing a progressive improvement in the methods used to identify and understand these aquatic characteristics. This study employs sea surface temperature data acquired from the Copernicus Marine and Environment Monitoring Service (CMEMS) in the Atlantic Ocean. The objective is to present EddyNet, a cutting-edge deep-learning framework specifically developed for the automatic identification and categorization of ocean eddies. EddyNet incorporates a pixel-wise classification layer into its neural encoder-decoder architecture. The resulting output is a map that maintains the same dimensions as the input, but each individual pixel is assigned a label indicating its classification as either “0” for noneddy regions, “1” for anticyclonic eddies, or “2” for cyclonic eddies. We propose a new image segmentation method based on the U-net architecture with different convolutional neural network backbones such as VGG16, VGG19, DenseNet121, and MobileNetV2. Our models are built and trained using Python and the Keras library with the Adam optimizer for improved convergence. Our approach uses sparse categorical cross-entropy as the loss function, simplifying the label encoding process for multiclass classification with sparse labels. Initial results show that this method achieves a good balance between computational efficiency and segmentation accuracy, making it suitable for real-time applications.
      PubDate: MON, 20 MAY 2024 09:15:55 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Modeling the Influence of Precipitation on L-Band SMAP Observations of
           Ocean Surfaces Through Machine Learning Approach

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      Authors: Xuchen Jin;Xianqiang He;Palanisamy Shanmugam;Jianyun Ying;Fang Gong;Qiankun Zhu;Delu Pan;
      Pages: 10291 - 10305
      Abstract: A new forward model (FM) was developed to characterize the influence of precipitation on L-band passive ocean surface measurements. The FM, which relates rain-induced brightness temperature (TB) variations to the rain rate and wind speed (WS), was established through a machine learning approach (referred to as the ML-FM). The soil moisture active passive (SMAP) data matched with integrated multisatellite retrievals for global precipitation measurement (IMERG) rain rate data and cross-calibrated multiplatform (CCMP) wind data were binned as a function of the rain rate, WS, and wind direction. The ML-FM was validated by comparing the simulated top-of-atmosphere (TOA) TB values with SMAP measurements. The results showed favorable agreement between the ML-FM outputs and SMAP data, with a root mean square error (RMSE) smaller than 0.55 K for both the horizontal and vertical polarizations. The validation results for ensuring more reasonable rainfall intensity distributions showed that the ML-FM returned stable results with a slightly reduced RMSE of ∼0.75 K for both the horizontal and vertical polarizations. Based on the ML-FM, we found that sea surface emission exhibited significant dependence on the rain rate for both polarizations. In addition, the ML-FM demonstrated signal saturation when the rain rate exceeded 45 mm/h, while precipitation slightly affected the directional characteristics of sea surface emission. These effects accounted for ∼0.3 K at a rain rate of 50 mm/h. Overall, our analyses demonstrated that the proposed ML-FM achieved superior performance in retrieving the TOA TB for both the vertical and horizontal polarizations with a higher accuracy than existing models.
      PubDate: TUE, 14 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Robust Unsupervised Multifeature Representation for Infrared Small Target
           Detection

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      Authors: Liqiong Chen;Tong Wu;Shuyuan Zheng;Zhaobing Qiu;Feng Huang;
      Pages: 10306 - 10323
      Abstract: Infrared small target detection is critical to infrared search and tracking systems. However, accurate and robust detection remains challenging due to the scarcity of target information and the complexity of clutter interference. Existing methods have some limitations in feature representation, leading to poor detection performance in complex scenes. Especially when there are sharp edges near the target or in cluster multitarget detection, the “target suppression” phenomenon tends to occur. To address this issue, we propose a robust unsupervised multifeature representation (RUMFR) method for infrared small target detection. On the one hand, robust unsupervised spatial clustering (RUSC) is designed to improve the accuracy of feature extraction; on the other hand, pixel-level multiple feature representation is proposed to fully utilize the target detail information. Specifically, we first propose the center-weighted interclass difference measure (CWIDM) with a trilayer design for fast candidate target extraction. Note that CWIDM also guides the parameter settings of RUSC. Then, the RUSC-based model is constructed to accurately extract target features in complex scenes. By designing the parameter adaptive strategy and iterative clustering strategy, RUSC can robustly segment cluster multitargets from complex backgrounds. Finally, RUMFR that fuses pixel-level contrast, distribution, and directional gradient features is proposed for better target representation and clutter suppression. Extensive experimental results show that our method has stronger feature representation capability and achieves better detection performance than several state-of-the-art methods.
      PubDate: WED, 08 MAY 2024 09:16:17 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Topological Building Extraction With Bidirectional Prediction From Remote
           Sensing Images

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      Authors: Mingming Zhang;Ye Du;Zhenghui Hu;Wei Wang;Qingjie Liu;Yunhong Wang;
      Pages: 10324 - 10337
      Abstract: Topological building extraction in remote sensing images is vital for city planning, disaster assessment, and other real-world applications. To meet the requirements of real-world applications, existing building extraction approaches predict topological building by vectorization of binary building masks using multiple refinement stages, leading to complex methodology and poor generalization. To tackle this issue, we propose a topological building extraction approach by directly predicting serialized vertices of each building instance. We observe that the order of serialized vertices from one building is inherently bidirectional, which can be clockwise or counterclockwise. By this new observation, the proposed method learns serialized vertices for each building supervised by the bidirectional constraint. Moreover, we design a cross-scale feature fusion module to obtain building representations with rich spatial and context information, facilitating the following serialized vertex prediction. Besides, a merge strategy is adopted to generate the final topological building from serialized vertices of two directions (clockwise and counterclockwise). Experiments are conducted on three building benchmarks to evaluate the effectiveness of our proposed method. Finally, extensive results show that the proposed approach outperforms state-of-the-art methods highlighting its superiority.
      PubDate: FRI, 10 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
  • On the Use of Azimuth Cutoff for Sea Surface Wind Speed Retrieval From SAR

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      Authors: Yuting Zhu;Giuseppe Grieco;Jiarong Lin;Marcos Portabella;Xiaoqing Wang;
      Pages: 10367 - 10379
      Abstract: The accurate retrieval of sea-surface wind field data is crucial for weather forecasting and climate modeling. Despite this, the complexity of sea surface conditions poses significant challenges for satellite-based synthetic aperture radar (SAR) wind retrieval techniques. This study introduces a Bayesian inversion algorithm that incorporates azimuth cutoff wavelength information—a parameter previously underutilized and highly sensitive to varying wind conditions. We aimed to enhance the accuracy of SAR-derived wind estimations to enable more reliable interpretations of marine atmospheric dynamics. The methodology probabilistically combines SAR data with ancillary meteorological information and optimizes the retrieval process through a cost function that leverages the sensitivity of the azimuth cutoff to changes in wind vector fields. The proposed method was comprehensively validated using Sentinel-1 and Gaofen-3 SAR datasets against buoy measurements and wind estimations from scatterometers. The results demonstrated that the proposed method significantly improved the accuracy of wind speed estimations, especially under low-wind conditions and different sea-state conditions, without substantially increasing the computational burden. Although the wind direction retrieval displayed limited enhancement, the improved accuracy in wind speed estimations provides considerable benefits for operational meteorological applications. These findings suggest that the integration of azimuth cutoff information could be a critical step toward obtaining more accurate and reliable wind field retrievals from SAR data, thereby advancing the field of remote sensing and oceanography.
      PubDate: THU, 30 MAY 2024 09:16:19 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Potential Landslide Identification Based on Improved YOLOv8 and InSAR
           Phase-Gradient Stacking

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      Authors: Yanrong Mao;Ruiqing Niu;Bingquan Li;Jun Li;
      Pages: 10367 - 10376
      Abstract: Landslides, as a major geological hazard, caused the significant threats to human life and property. Therefore, the identification of potential landslides is a crucial concern. This study combines synthetic aperture radar interferometry phase-gradient stacking with deep learning to achieve efficient and accurate identification of large-scale potential landslides. By stacking phase gradients to highlight local surface deformation information and refining the YOLOv8 model based on small target features of local deformations, this study introduces improvements. This involves adding a convolutional block attention module layer to the backbone, replacing the C2f modules with GhostNetV2 to suppress information loss during long-distance feature transmission, and enhancing the network's perception and detection capabilities for small targets. In addition, a 160 × 160 small target detection head is added to the detection module to specifically handle small target detection tasks, improving accuracy and performance. A new loss function, FocalSIoU loss, is introduced based on the characteristics of the dataset, combining SIoU with the gamma bias option to make the model more targeted during training. The improved model achieved a maximum mAP50 value of 93.4% on the validation set. Finally, using mask factors to identify region deformation points, this study reduces misjudgments of non-landslides, identifying 378 potential landslides in the study area with a false positive rate of only 10.2%.
      PubDate: MON, 13 MAY 2024 09:16:12 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Bitemporal Attention Sharing Network for Remote Sensing Image Change
           Detection

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      Authors: Zhongchen Wang;Guowei Gu;Min Xia;Liguo Weng;Kai Hu;
      Pages: 10368 - 10379
      Abstract: With the advancement of remote sensing image technology, the availability of very high-resolution image data has brought new challenges to change detection (CD). Currently, deep learning-based CD methods commonly employ bitemporal interaction networks using convolutional neural networks or transformers. Yet, these models overly emphasize object accuracy, leading to a significant increase in computational costs with limited performance gains. In addition, the current bitemporal interaction mechanisms are simplistic, failing to adequately account for spatial positions and scale variations of different objects, resulting in an inaccurate modeling of dynamic feature changes between images. To address these issues, a bitemporal attention sharing network is proposed, which tackles the problems effectively by making bitemporal and multiscale attention sharing the primary mode of feature interaction. Specifically, the proposed bitemporal attention sharing module leverages pairs of features preliminarily encoded by a backbone to construct shared global features, directing attention to target changes. Then, through cross-scale attention guidance and weighted fusion, it achieves attention sharing of multiscale features, eliminating the need for overrelying on deep convolutional layers for feature extraction. Experiments on three public datasets demonstrate that, in comparison to several state-of-the-art methods, our model achieves superior performance with low computational cost.
      PubDate: TUE, 14 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Remote Sensing Inversion of the Total Suspended Matter Concentration in
           the Nanyi Lake Based on Sentinel-3 OLCI Imagery

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      Authors: Yong Xie;Yanting Zhou;Zui Tao;Wen Shao;Meng Yang;
      Pages: 10380 - 10389
      Abstract: Herein, in situ water reflectance and TSM data obtained from several experiments on the Nanyi Lake from 2018 to 2022 and the Sentinel-3 Ocean and Land Colour Instrument (OLCI) satellite synchronization data were used to compare four atmospheric correction methods (FLAASH, 6S, ACOLITE, and C2RCC) and construct an empirical model for TSM inversion in the Nanyi Lake to analyze the spatial and temporal changes in the water quality in the Nanyi Lake from 2018 to 2023. On the Sentinel-3 OLCI data, the C2RCC algorithm showed the highest accuracy and overall performance stability (RMSE: 0.0014–0.0051 Sr−1, MAPE: 18.44%–68.47%, and BIAS: from −3.68% to 23.63%). The highest correlation was observed between the three-band ratio (B9 + B18)/B10 and the in situ TSM; the TSM inversion model constructed based on this inversion factor showed the best accuracy for the Nanyi Lake (R2: 0.76, RMSE: 5.01 mg/L, and MAPE: 28.46%). The spatial and temporal changes in TSM in the Nanyi Lake exhibited significant regularity. Specifically, the TSM was higher in 2018–2019, significantly decreased in 2020, and stabilized in 2021–2023. Owing to the effects of human activities, precipitation, and illumination, seasonal variation in the TSM in the Nanyi Lake was detected, with TSM decreasing in the following order: summer> autumn> spring> winter. Concerning spatial variations, high TSM was observed in the northwest, northeast, and southeast of the Nanyi Lake reclamation area and its surrounding lake area. River confluence and human activities affected the area, leading to significant fluctuations in TSM in 2018–2023.
      PubDate: MON, 20 MAY 2024 09:15:55 -04
      Issue No: Vol. 17, No. null (2024)
       
  • A Cascaded Network With Coupled High-Low Frequency Features for Building
           Extraction

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      Authors: Xinyang Chen;Pengfeng Xiao;Xueliang Zhang;Dilxat Muhtar;Luhan Wang;
      Pages: 10390 - 10406
      Abstract: Accurately extracting buildings from high-resolution remote sensing images is crucial for human productivity and livelihood in urban areas. Due to varying scales and indistinct boundaries of buildings, it is crucial to fully leverage the high- and low-frequency features in building extraction from remote sensing images. However, previous studies have solely relied on either low- or high-frequency features, leading to errors such as omissions or internal holes in the detected buildings at various scales. Although some studies have considered the integration between both high- and low-frequency features, they overlook the suitability of different network depths for extracting different frequency features. A novel network called Cascaded Inception Conv-Former Network (CICF-Net) is proposed in this study to solve these problems. It leverages the parallel combination of convolutional neural network and Transformer to efficiently extract high- and low-frequency features for building extraction. In the encoder, as the network depth grows, we gradually reduce the contribution of high-frequency branch and enhance the focus on low-frequency branch. Moreover, a cascaded fusion strategy is employed to extract and integrate multiscale high- and low-frequency features. Meanwhile, we propose gated convolution UperNet as the decoder, which utilizes recursive gated convolution to facilitate multilevel spatial interactions and better restoration of fine-grained spatial details for building segmentation. The proposed CICF-Net achieves competitive accuracies on three public benchmarks: Massachusetts Building Dataset, WHU Aerial Building Dataset, and Inria Aerial Image Labeling Dataset, with IoU of 75.17%, 91.45%, and 81.28%, respectively. This provides strong evidence of its effectiveness in building extraction, as it can accurately capture spatial details and context of buildings.
      PubDate: TUE, 21 MAY 2024 09:15:50 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Radiometric Cross Calibration of HY-1C/COCTS Based on Sentinel-3/OLCI

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      Authors: Yong Xie;Duo Feng;Wen Shao;Jie Han;Yidan Chen;
      Pages: 10422 - 10431
      Abstract: The on-orbit radiometric calibration of spaceborne sensors is the foundation of quantitative remote sensing, and the calibration accuracy directly affects the quality of quantitative remote sensing products. The wide swath of the Chinese Ocean Color and Temperature Scanner (COCTS) onboard the HY-1C satellite makes it difficult to ignore errors caused by viewing angles during calibration. Thus, this article proposes a cross-calibration method based on the bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) spectral band adjustment factors (SBAFs). This method cross calibrates the COCTS sensor after correcting viewing geometry and radiometric differences. It is based on the Sentinel-3/ocean and land color instrument (OLCI) sensor. First, time-series data from the OLCI sensor were used to select calibration points in the Dunhuang area. Subsequently, the bidirectional reflectance distribution function (BRDF) for each corresponding spectral band of the OLCI sensor was constructed point by point. Next, the BOA and TOA SBAFs were calculated using the interpolated continuous spectra obtained from MOD09GA products. After correcting for the BRDF and SBAF, cross calibration between COCTS and OLCI was achieved. The results showed that the BOA SBAF model achieved higher precision calibration coefficients than the TOA SBAF model, with errors in each band below 5.76%. The proposed method can provide technical support for the cross calibration of wide-swath sensors.
      PubDate: MON, 20 MAY 2024 09:15:55 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Refined STACK-CNN for Meteor and Space Debris Detection in Highly Variable
           Backgrounds

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      Authors: Leonardo Olivi;Antonio Montanaro;Mario Edoardo Bertaina;Antonio Giulio Coretti;Dario Barghini;Matteo Battisti;Alexander Belov;Marta Bianciotto;Francesca Bisconti;Carl Blaksley;Sylvie Blin;Karl Bolmgren;Giorgio Cambiè;Francesca Capel;Marco Casolino;Igor Churilo;Marino Crisconio;Christophe De La Taille;Toshikazu Ebisuzaki;Johannes Eser;Francesco Fenu;George Filippatos;Massimo Alberto Franceschi;Christer Fuglesang;Alessio Golzio;Philippe Gorodetzky;Fumiyoshi Kajino;Hiroshi Kasuga;Pavel Klimov;Viktoria Kungel;Vladimir Kuznetsov;Massimiliano Manfrin;Laura Marcelli;Gabriele Mascetti;Włodzimierz Marszał;Marco Mignone;Hiroko Miyamoto;Alexey Murashov;Tommaso Napolitano;Hitoshi Ohmori;Angela Olinto;Étienne Parizot;Piergiorgio Picozza;Lech Wiktor Piotrowski;Zbigniew Plebaniak;Guillaume Prévôt;Enzo Reali;Marco Ricci;Giulia Romoli;Naoto Sakaki;Sergei Sharakin;Kenji Shinozaki;Jacek Szabelski;Yoshiyuki Takizawa;Valerio Vagelli;Giovanni Valentini;Michal Vrabel;Lawrence Wiencke;Mikhail Zotov;
      Pages: 10432 - 10453
      Abstract: In this article, we present cutting-edge machine learning-based techniques for the detection and reconstruction of meteors and space debris in the Mini-EUSO experiment, a detector installed on board of the International Space Station, and pointing toward the Earth. We base our approach on a recent technique, the STACKing method plus Convolutional Neural Network (STACK-CNN), originally developed as an online trigger in an orbiting remediation system to detect space debris. Our proposed method, the refined-STACKing method plus convolutional neural network (R-Stack-CNN), makes the STACKing method plus convolutional neural network (STACK-CNN) more robust, thanks to a random forest that learns the temporal development of these events in the camera. We prove the flexibility of our method by showing that it is sensitive to any space object that moves linearly in the field of view. First, we search small space debris, never observed by Mini-EUSO. Due to the limiting statistics, also in this case, no debris were found. However, since meteors produce signals similar to space debris but they are much more frequent, the R-Stack-CNN is adapted to identify such events while avoiding the numerous false positives of the Stack-CNN. Results from real data show that the R-Stack-CNN is able to find more meteors than a classical thresholding method and a new method of two neural networks. We also show that the method is also able to accurately reconstruct speed and direction of meteors with simulated data.
      PubDate: TUE, 07 MAY 2024 09:16:17 -04
      Issue No: Vol. 17, No. null (2024)
       
  • An Efficient and Fast Image Mosaic Approach for Highway Panoramic UAV
           Images

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      Authors: Haoxin Zheng;Zhanqiang Chang;Yakai Li;Jie Zhu;Wei Wang;Qing Yang;Chou Xie;Jingfa Zhang;Jiaxi Liu;
      Pages: 10454 - 10467
      Abstract: Nowadays, real-time monitoring of highway operation by unmanned aerial vehicle (UAV) technology is one of the research frontiers for urban remote sensing. In general, the existing stitching algorithms can meet the basic requirements in terms of accuracy, but their splicing speed cannot meet the real-time stitching requirements of UAV. The cause is that the time consumption sharply increases when stitching plenty of UAV images—this is the bottleneck problem. Herein, we proposed a novel splicing method based on the Superpoint network and a self-designed algorithm of matrix iteration. In this method, we take advantage of an advanced deep learning algorithm—Superpoint to efficiently extract image feature points for calculating the geometric transformation matrix, and make the Superpoint model more suitable for highway. More importantly, for the purpose of further improving the stitching speed and realizing real-time stitching for a large number of UAV images, we specially designed an algorithm of matrix iteration to accurately represent the image transformation relationships, i.e., a matrix is iterated through each adjacent transformation matrix relationship. It is the first time that an algorithm of transformation matrix iteration has been designed to address the bottleneck problem in stitching plenty of UAV images. As a result, the experiments indicate that the proposed method has remarkably enhanced the stitching speed and accuracy for plenty of UAV images. Notably, even in the condition of no air triangulation parameters, it can realize real-time stitching.
      PubDate: FRI, 24 MAY 2024 09:15:52 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Near-Field Geoacoustic Inversion Using Bottom Reflection Signals via
           Self-Attention Mechanism

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      Authors: Yuxuan Ma;Xiaobo Zhang;Fukun Jiang;Zhengrong Wei;Chenguang Liu;
      Pages: 10545 - 10558
      Abstract: Geoacoustic inversion typically involves the collection of far-field underwater acoustic data to obtain seabed geoacoustic parameters using empirical formulas and matched field inversion (MFI) techniques. However, acoustic data propagated over long distances can introduce inevitable errors in inversion results, and traditional MFI techniques suffer from low computational efficiency. Although deep learning technologies have been applied to geoacoustic inversion, conventional deep neural network (DNN) models struggle to capture the long- and short-term dependencies in bottom reflection data, leading to suboptimal inversion accuracy. These issues present challenges in rapidly and accurately acquiring geoacoustic parameters over large areas. To address this, we propose a near-field bottom reflection signal collection method, collecting bottom reflection signals over a wide range of grazing angles by drifting. Utilizing the characteristics of near-field sound propagation, we constructed the bottom reflection coefficient sequence dataset using the wavenumber integration method. We then introduce a novel deep learning model, self-attention geoacoustics, based on multihead self-attention mechanisms, which improves inversion accuracy. In addition, we propose an adaptive-weight multitask learning training strategy, significantly enhancing the prediction accuracy of sound attenuation. Experimental results demonstrate that our method outperforms conventional geoacoustic inversion methods based on MFI and DNNs in terms of efficiency and accuracy, proving the superiority of our approach.
      PubDate: FRI, 24 MAY 2024 09:15:52 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Inversion of Reservoir Parameters for Oil Extraction Based on Deformation
           Monitoring With InSAR

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      Authors: Yue Ma;Lu Zhang;Ding Chen;Haolei Zhang;Yitong Zheng;Shiyong Yan;
      Pages: 10559 - 10568
      Abstract: Frequent water injection and oil extraction can potentially lead to changes in reservoir pressure, burst or collapse of casings, and corresponding variations on the ground surface. Attention should be focused on developing the strategies to monitor and mitigate the adverse consequences of oilfield operations. Here, the distributed scatterer InSAR (DS-InSAR) technique is applied to estimate the surface deformation of Daqing oil field with the surveillance period from 2015 to 2019. Furthermore, Bayesian method with three models, including Mogi, prolate spheroid, and Okada, are implemented to obtain parameters related to underground cavities, using cumulative deformation recorded between June and December 2015 and May and December 2016 as input. Inversion validation measurements were carried out by comparing the results with field data in the area of interest. Our findings demonstrate that, in comparison to other models, the prolate spheroid could recover both reservoir pressure and surface deformation, offering compelling evidence in favor of mitigating shear failure-related issues. A dual-source prolate spheroid model is also used to simulate large-scale deformation on the surface of oilfield. Its characteristics are nearly in line with variations in reservoir pressure and casing depth, which may minimize the negative consequences of casing failures and guarantee the stability of oil production.
      PubDate: TUE, 28 MAY 2024 09:15:39 -04
      Issue No: Vol. 17, No. null (2024)
       
  • IA-CIOU: An Improved IOU Bounding Box Loss Function for SAR Ship Target
           Detection Methods

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      Authors: Pingping Huang;Shihao Tian;Yun Su;Weixian Tan;Yifan Dong;Wei Xu;
      Pages: 10569 - 10582
      Abstract: Ship detection in synthetic aperture radar (SAR) images is crucial in both civilian and military fields, offering extensive application prospects. Nonetheless, owing to the distinctive characteristics of SAR imaging, this task confronts numerous challenges. Specifically, ships with high aspect ratios, dense arrangements and small sizes in complex environments frequently yield in suboptimal positioning effects, consequently impacting detection performance. In response to the challenges in ship target detection, this article introduces a novel approach, termed Inner-alpha-CIOU (IA-CIOU), that relies on an enhanced intersection over union (IOU). Primarily, the method introduces Inner IOU, which effectively regulates generation of auxiliary bounding boxes through scale factor r. This ensures a better fit for dimensions of ship target frames, thereby enhancing target detection performance as well as expediting model convergence. Subsequently, this method introduces Alpha IOU, enhancing robustness of small-size ship targets in complex backgrounds by adjusting α. This allows the detector to achieve greater flexibility in ship regression accuracy. Following numerous experimental validations, proposed algorithm consistently outperforms on both SAR-Ship-Dataset, MSAR-1.0 dataset, and SAR ship detection dataset (SSDD) dataset. This groundbreaking innovation not only possesses immeasurable practical worth, but also introduces a fresh perspective together with enlightening insights for future research efforts.
      PubDate: FRI, 17 MAY 2024 09:15:47 -04
      Issue No: Vol. 17, No. null (2024)
       
  • A Multisource Dynamic Fusion Network for Urban Functional Zone
           Identification on Remote Sensing, POI, and Building Footprint

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      Authors: Hangfeng Qiao;Huiping Jiang;Gang Yang;Faming Jing;Weiwei Sun;Chenyang Lu;Xiangchao Meng;
      Pages: 10583 - 10599
      Abstract: Urban functional zones (UFZ) identification with remote sensing imagery (RSI) is attracting increasing attention in urban planning and resource allocation in urban areas, etc. The UFZ is a comprehensive unit comprising geographical, how to effectively integrate the RSI and points of interest (POI) with different physical and socioeconomic characteristics is important and promising. However, there are two challenges for the UFZ identification. On one hand, the UFZ is closely related to buildings, and most current methods lack an in-depth understanding of building semantics. Therefore, an efficient integration of building footprint (FT) data deserves further investigation. On the other hand, these RSI, POI, and FT data are heterogeneous; how to effectively leverage complementary information among these highly heterogeneous modalities to enhance the comprehensive understanding of urban. To solve the above challenges, this article introduces an end-to-end deep learning-based multisource dynamic fusion network for UFZ identification on RSI, POI, and FT. In the proposed method, an adaptive weight interactive fusion module is designed to comprehensively integrate the complementary information among the heterogeneous RSI, POI, and FT data sources. In addition, a multiscale feature focus module is proposed to extract multiscale image features and emphasize critical characteristics. This method was applied to UFZ classification in Ningbo, Zhejiang Province, China, and the experimental results demonstrate the competitive performance.
      PubDate: FRI, 31 MAY 2024 09:16:29 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Warm-Season Microwave Integrated Retrieval System Precipitation
           Improvement Using Machine Learning Methods

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      Authors: Shuyan Liu;Christopher Grassotti;Quanhua Liu;
      Pages: 10600 - 10611
      Abstract: This study compares the performance of five selected machine learning models regarding precipitation climatology during the warm season in 2022 and 2023 over the continental U.S. Input features included retrieved products from the microwave integrated retrieval system (MiRS) based on NOAA-20 ATMS data. The radar-based instantaneous multiradar multisensor system precipitation was used for model training and validation. Among the models, three used a U-Net architecture and two used a deep neural network (DNN) architecture. The U-Net models all significantly outperformed the DNN models for the evaluated metrics. While the DNN architecture can only learn from local inputs, the U-Net also has the capability to learn from neighborhood spatial patterns. As such, the DNN overcorrected the precipitation amounts that MiRS had overestimated, leading to net underestimation, but also failed to improve the overall performance relative to the original MiRS estimates. The U-Net not only corrected MiRS overestimation in the central U.S., but also improved the MiRS dry bias over the Southeast. Among the five experiments, the one that used the MiRS retrieved column-integrated hydrometeors of graupel water path, rainwater path, cloud liquid water, total precipitable water, and geolocation information demonstrated the best performance, improving the MiRS spatial correlation coefficient from 0.75 to 0.89 and reducing the mean bias percentage from 11.95% to −6.33% for 2022 accumulated precipitation. This suggests that applying an appropriate architecture and input features provides an opportunity to determine more accurate physical and statistical relationships which can include spatial and regional dependence, leading to improved microwave-based precipitation estimates.
      PubDate: MON, 27 MAY 2024 09:15:42 -04
      Issue No: Vol. 17, No. null (2024)
       
  • F3Net: Feature Filtering Fusing Network for Change Detection of Remote
           Sensing Images

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      Authors: Junqing Huang;Xiaochen Yuan;Chan-Tong Lam;Guoheng Huang;
      Pages: 10621 - 10635
      Abstract: Change detection of remote sensing images is an essential method for observing changes on the Earth's surface. Deep learning can efficiently process remote sensing images. However, shallow features in remote sensing data from different time are inherently inconsistent. During the feature extraction stage, these shallow features are mapped onto different dimensional feature maps, giving rise to noise information. Existing algorithms are ineffective in dealing with noise effectively. This can lead to detection results being influenced by shallow features noise information, resulting in fake detections. To address this issue, feature filtering fusing network (F3Net) is proposed in this article. In F3Net, feature filtering and aggregation (FFA) module is designed to integrate bitemporal remote sensing features, which initially filters out noise information from different temporal domains. In addition, the channel feature difference fusion (CFDF) module is introduced to fuse high-dimensional features. Within CFDF, channel information filtering convolution is utilized to filter out noise information from high-dimensional feature channels across multiple receptive fields. In order to verify the performance of F3Net, comparative experiments were conducted on multiple public datasets with other state-of-the-art models, and F3Net achieved the best performance.
      PubDate: TUE, 28 MAY 2024 09:15:39 -04
      Issue No: Vol. 17, No. null (2024)
       
  • Digital Surface Model Super-Resolution by Integrating High-Resolution
           Remote Sensing Imagery Using Generative Adversarial Networks

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      Authors: Guihou Sun;Yuehong Chen;Jiamei Huang;Qiang Ma;Yong Ge;
      Pages: 10636 - 10647
      Abstract: Digital surface model (DSM) is the fundamental data in various geoscience applications, such as city 3-D modeling and urban environment analysis. The freely available DSM often suffers from limited spatial resolution. Super-resolution (SR) is a promising technique to increase the spatial resolution of DSM. However, most existing SR models struggle to reconstruct spatial details, such as buildings, valleys, and ridges. This article proposes a novel DSM super-resolution (DSMSR) model that integrates high-resolution remote sensing imagery using generative adversarial networks. The generator in DSMSR contains three modules. The first DSM feature extraction module uses the residual-in-residual dense block to extract features from low-resolution DSM. The second multiscale attention feature extraction module employs the pyramid convolutional residual dense blocks to capture the spatial details of ground objects at multiple scales from remote sensing imagery. The third DSM reconstruction module uses a squeeze-and-excitation block to fuse the extracted features from low-resolution DSM and high-resolution remote sensing imagery for generating SR DSM. The discriminator of DSMSR uses the relativistic average discriminator for adversarial learning. The slope loss is further introduced to ensure the accurate representation of topographic features. We evaluate DSMSR on four different terrain regions in the U.K. to downscale the 30-m AW3D30 DSM to 5-m DSM. The experimental results indicate that DSMSR outperforms the traditional interpolation algorithms and four existing deep-learning-based SR models. The DSMSR restores more spatial detail of topographic features and generates more accurate image quality, elevation, and terrain metrics.
      PubDate: FRI, 10 MAY 2024 09:15:56 -04
      Issue No: Vol. 17, No. null (2024)
       
 
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School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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