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IEEE Geoscience and Remote Sensing Letters
Journal Prestige (SJR): 1.486
Citation Impact (citeScore): 4
Number of Followers: 192  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1545-598X
Published by IEEE Homepage  [191 journals]
  • [Front cover]
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • IEEE Geoscience and Remote Sensing Letters publication information
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • IEEE Geoscience and Remote Sensing Letters information for authors
    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • IEEE Geoscience and Remote Sensing Letters Institutional Listings
    • Abstract: Advertisements.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • An Adaptive Thunderstorm Measurement Concept Using C-Band and X-Band Radar
           Data
    • Authors: Jacopo Grazioli;Andreas Leuenberger;Lionel Peyraud;Jordi Figueras I Ventura;Marco Gabella;Alessandro Hering;Urs Germann;
      Pages: 1673 - 1677
      Abstract: This letter presents a proof of concept illustrating the possibility to integrate X-band radar scans of individual thunderstorm cells within an operational context of storm cells identification, tracking, and severity ranking performed by a long-range volume-scanning surveillance C-band radar network. The X-band radar performs adaptive 3-D scans of one automatically selected cell, i.e., the most intense storm cell within its domain, previously identified by the operational C-band radars. The scan aims to observe with high spatiotemporal resolution the core of the cell and the evolution of its vertical structure, providing a unique data set for microphysical and dynamical interpretations. The cells tracked with this method by the X-band radar can be characterized with a high spatiotemporal resolution, better with respect to the operational C-band measurements, as illustrated in the letter with a case study of a supercellular storm occurring in western Switzerland on June 7, 2015. This method is a valuable example of the potential added value of an X-band radar system of flexible scanning strategy, in addition to C-band operational measurements conducted with a fixed scanning protocol.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Improving CHIRPS Daily Satellite-Precipitation Products Using Coarser
           Ground Observations
    • Authors: Weiyue Li;Weiwei Sun;Xiaogang He;Marco Scaioni;Dongjing Yao;Yu Chen;Jun Gao;Xin Li;Guodong Cheng;
      Pages: 1678 - 1682
      Abstract: A clear bias exists in the widely used gridded precipitation products (GPPs) that result from factors about topography, climate, and retrieval algorithms. Many existing optimization works have a deficiency in validation and domain sizes, which makes the evaluation and corrections of the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) product still challenging. In this letter, we propose a bias-correction approach that combines coarser-resolution gauge-based precipitation with a probability distribution function (PDF) to improve the accuracy of CHIRPS. The data from 27 local precipitation gauges in Shanghai are utilized to testify the performance of our method. Results explain that daily corrected CHIRPS (Cor-CHIRPS) product has higher accuracy than CHIRPS compared with ground truths (GrTs) in terms of both error statistics and detection capability, particularly in spring, autumn, and winter. Moreover, Cor-CHIRPS better captures the frequencies of precipitation events and well depicts the spatial characteristics of the annual precipitation.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Fast Induction Logging Modeling With Hierarchical Sudoku Meshes Based on
           DGFD
    • Authors: Runren Zhang;Qingtao Sun;Zhenguan Wu;Yuan Fang;Yunyun Hu;Wei-Feng Huang;Yiqian Mao;Qing Huo Liu;
      Pages: 1683 - 1687
      Abstract: This letter extends the discontinuous Galerkin frequency domain (DGFD)-based domain decomposition method (DDM) to model the open borehole environment accurately with a newly developed hierarchical sudoku mesh. Both the borehole and mud invasion effects can be modeled by this framework efficiently. Furthermore, deep reading measurements can be modeled conveniently by inserting arbitrarily shaped objects into the hierarchical sudoku mesh; the capability to distinguish a cavity saturated with either oil or water with different borehole-object distances are then studied for a deep reading tool in an open borehole carbonate environment. The DGFD with the hierarchical sudoku mesh shows six times faster speed than the traditional finite-element method (FEM) for the deep reading case.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Extracting Reflected Waves From Acoustic Logging Data Based on the
           Shearlet Transform
    • Authors: Jia Chen;Wenzheng Yue;Chao Li;Fuqiang Zeng;
      Pages: 1688 - 1692
      Abstract: Acoustic reflection imaging in well logging is an important technology for surveying near-borehole geological structures, especially for exploration in heterogeneous reservoirs. The key component of this method is the extraction of reflected waves from full waveforms. Due to the high impedance of the borehole wall interface, it is quite difficult to separate reflections from the dominant mode waves. This letter introduces a method for identifying reflected waves based on the shearlet transform. This method, which is characterized by multiscale and multidirectional decomposition capabilities, can be used to identify different components of acoustic logging signals. The effectiveness and accuracy of the proposed method are verified in comparison with other methods with synthetic data. Finally, field data results further validate this method.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Unsupervised Clustering of Seismic Signals Using Deep Convolutional
           Autoencoders
    • Authors: S. Mostafa Mousavi;Weiqiang Zhu;William Ellsworth;Gregory Beroza;
      Pages: 1693 - 1697
      Abstract: In this letter, we use deep neural networks for unsupervised clustering of seismic data. We perform the clustering in a feature space that is simultaneously optimized with the clustering assignment, resulting in learned feature representations that are effective for a specific clustering task. To demonstrate the application of this method in seismic signal processing, we design two different neural networks consisting primarily of full convolutional and pooling layers and apply them to: 1) discriminate waveforms recorded at different hypocentral distances and 2) discriminate waveforms with different first-motion polarities. Our method results in precisions that are comparable to those recently achieved by supervised methods, but without the need for labeled data, manual feature engineering, and large training sets. The applications we present here can be used in standard single-site earthquake early warning systems to reduce the false alerts on an individual station level. However, the presented technique is general and suitable for a variety of applications including quality control of the labeling and classification results of other supervised methods.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Irradiance Field Reconstruction From Partial Observability of Solar
           Radiation
    • Authors: Jonatan Ostrometzky;Andrey Bernstein;Gil Zussman;
      Pages: 1698 - 1702
      Abstract: Photovoltaic (PV) panels have become a significant source of electric power generation. These panels are considered to be one of the cleanest energy production systems available, so their spread is expected to increase in the following years, especially because recent technologies have reduced the cost of these panels. Unlike classic energy production methodologies that are connected to the high-voltage transmission power lines, many PV panels are connected directly to the lower voltage distribution networks of the electric power grid, making the management of the grid an ongoing challenge. In this letter, we address this challenge and show that the irradiance field that is required to calculate the expected power output of the PV panels can be estimated in a simplistic methodology, using partial observability of the solar radiation. We validate our proposed methodology by conducting an empirical study that uses real data of the solar radiation taken from satellites, and we show that even when the observability of the solar radiation is as low as 10% (meaning that only one in ten points of interest in a regular grid is observable), the irradiance field can be accurately estimated.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • On-Orbit Self-Focusing Using Conjugated Optical Fiber Waveguides for Space
           Optical Cameras
    • Authors: Jin Li;Fei Xing;Pawan Kumar Shrestha;Fengyuan Shi;Zilong Liu;
      Pages: 1703 - 1705
      Abstract: High-precision focusing of space optical cameras is the prerequisite for capturing high-quality images in earth observation applications. On-orbit focusing methods are basically based on image-processing algorithms. However, these focusing methods have several limitations, such as calculation complexity, low reliability, and high susceptibility to noises. We propose a real-time self-focusing method using conjugated optical fiber waveguides, which can measure the focal length of on-orbit cameras in real time. We use two conjugated optical fiber waveguides. At one end of each waveguide, there is a light source located external to the camera. The light is guided into the focal plane of the camera and it acts as an infinite point target. Contrary to image processing algorithm, our method is based on the optical components for focusing.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Analysis of Damage to Buildings in Urban Centers on Unstable Slopes via
           TerraSAR-X PSI Data: The Case Study of El Papiol Town (Spain)
    • Authors: D. Peduto;G. Nicodemo;M. Cuevas-Gonzáles;M. Crosetto;
      Pages: 1706 - 1710
      Abstract: Persistent Scatterer Interferometry (PSI) data, deriving from the processing of SAR images acquired by high-resolution sensors such as TerraSAR-X, provide accurate measurements of displacements affecting structures (e.g., buildings) and linear infrastructure networks (e.g., roads, bridges, embankments, and pipelines). Such widespread displacements, when available on buildings on unstable slopes, offer new perspectives for their integration in procedures pursuing the analysis and the prediction of the physical vulnerability of exposed buildings. In this letter, both deterministic and probabilistic cause (differential settlements)—effects (damage) relationships are generated by using PSI-derived building settlements and the results of building damage surveys. The procedure is applied to El Papiol town (Spain), whose urban area has been suffering diffuse damage of different severity to buildings and roads due to extremely slow-moving landslide phenomena.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • An Efficient Eigenstructure-Based Coherence Measure via Dimensionality
           Reduction
    • Authors: Zhizhou Huo;Xiwu Liu;Xiaokai Wang;Wenchao Chen;
      Pages: 1711 - 1715
      Abstract: The eigenstructure-based coherence measure has been widely used in characterizing faults and other discontinuous structures in seismic quantitative interpretation. However, it still suffers from the high computational cost which is caused by the eigendecomposition of one large covariance matrix. In this work, we focus on the computational cost of the widely-used coherence measure and try to propose one efficient eigenstructure-based coherence measure but without lowering the visual quality. We replace calculating one large covariance matrix’s dominant eigenvalue with calculating two smaller covariance matrixes’ dominant eigenvalues and use these two dominant eigenvalues to construct one efficient eigenstructure-based coherence measure. The computational cost of the proposed efficient eigenstructure-based coherence measure is smaller than the computational cost of the common eigenstructure-based coherence measure but without decreasing visual quality. Finally, we use one 3-D field data example to illustrate the proposed efficient eigenstructure-based coherence measure’s efficiency.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • High-Order Generalized Orderless Pooling Networks for Synthetic-Aperture
           Radar Scene Classification
    • Authors: Kang Ni;Peng Wang;Yiquan Wu;
      Pages: 1716 - 1720
      Abstract: Fixed coding style in bag of visual words (BOVW) model and strong spatial information in convolutional neural network (CNN) feature representation make the feature vector less adaptable for scene classification. With the purpose of extracting the learnable orderless feature for SAR scene classification, the high-order generalized orderless pooling network trained by backpropagation is proposed for learning the high-order vector of locally aggregated descriptors (VLADs) and locality constrained affine subspace coding (LASC), compared with the first-order feature coding style, the proposed network could learn high-order coding features by outer product automatically. Subsequently, for making the feature representation more powerful, the matrix normalization (square root) whose gradients are computed via singular value decomposition (SVD) and elementwise normalization are introduced into the proposed network. Finally, experiments on the SAR scene classification data set from TerraSAR-X image show the proposed networks achieve better performance than the state-of-the-art approaches.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Analysis and Improvement of Direct Sampling Method in the Monostatic
           Configuration
    • Authors: Sangwoo Kang;Marc Lambert;Won-Kwang Park;
      Pages: 1721 - 1725
      Abstract: The recently introduced noniterative imaging method entitled “direct sampling method” (DSM) is known to be fast, robust, and effective for inverse scattering problems in the multistatic configuration but fails when applied to the monostatic one. To the best of our knowledge, no explanation of this failure has been provided yet. Thanks to the framework of the asymptotic and the far-field hypothesis in the 2-D scalar configuration, an analytical expression of the DSM indicator function in terms of the Bessel function of order zero and sizes, shapes, and permittivities of the inhomogeneities is obtained and the theoretical reason of the limitation identified. A modified version of DSM is then proposed in order to improve the imaging method. The theoretical results are supported by numerical results using synthetic data.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Inversion of the Slip Distribution of an Earthquake From InSAR Phase
           Gradients: Examples Using Izmit Case Study
    • Authors: Alessandro Parizzi;
      Pages: 1726 - 1730
      Abstract: This letter investigates the estimation of the slip distribution of a seismic event using the information provided by interferometric phase gradients. Even if the technique is expected to be suboptimal when compared with an estimation using the unwrapped interferometric phase, such an approach would permit to avoid the solution of phase ambiguities also including the parts of the interferogram that could not be reached by the phase unwrapping otherwise. This specifically addresses the cases where the motion gradients are so strong that particular areas have to be masked out due to unwrapping errors. Aim of this letter is to propose a possible way to include such areas modeling the motion with phase gradients. The rationale of this letter relies on the description of the coseismic motion given by the Okada model that provides both the 3-D surface displacement and the gradient tensor information. Based on the latter, this letter defines an inversion strategy that uses the information extracted by the phase gradients, hence avoiding phase unwrapping. This technique is tested on real test sites and compared with the results obtained using the absolute phase.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Spaceborne Demonstration of Distributed SAR Imaging With TerraSAR-X and
           TanDEM-X
    • Authors: Thomas Kraus;Gerhard Krieger;Markus Bachmann;Alberto Moreira;
      Pages: 1731 - 1735
      Abstract: Multistatic or distributed satellite systems offer new and unique capabilities necessary for Earth observation with high spatial and temporal resolution. This letter describes a multistatic synthetic aperture radar (SAR) experiment employing the satellites TerraSAR-X and TanDEM-X. In order to demonstrate distributed SAR imaging from space, special data acquisitions with a dedicated geometry were performed. The data evaluation approach is outlined and azimuth profiles over a region with high-contrast backscatter are used to evaluate the azimuth signal reconstruction performance. The results are verified with simulations performed with a flexible SAR simulator reproducing the acquired scene. Finally, the effect of target motion on the reconstruction is analyzed and discussed based on the experimental data.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Characteristics of the L-Band Radio Frequency Interference Environment
           Based on SMAP Radiometer Observations
    • Authors: Hamid Rajabi;Mustafa Aksoy;
      Pages: 1736 - 1740
      Abstract: The performance of radio frequency interference (RFI) detection and mitigation algorithms depends on the properties of the RFI signals against which they are used. This letter presents the bandwidth, duration, and center frequency of the RFI signals that the Soil Moisture Active Passive (SMAP) radiometer has observed in the course of one week (June 3–9, 2018) over the entire world. It has been shown that L-band RFI is a significant problem on a global scale, and SMAP multidomain RFI detection approach supported by its digital backend is well justified as the bandwidth and duration characteristics of the RFI environment may vary significantly.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Hyperspectral Image Clustering Based on Unsupervised Broad Learning
    • Authors: Yi Kong;Yuhu Cheng;C. L. Philip Chen;Xuesong Wang;
      Pages: 1741 - 1745
      Abstract: Due to the difficulty of labeling a large number of training samples of a hyperspectral image (HSI), unsupervised clustering methods have drawn great attention. The recently proposed broad learning (BL) can implement both linear and nonlinear mappings. However, the original BL is a supervised model. In this letter, a novel method named unsupervised BL (UBL) is introduced for HSI clustering. First, a graph-regularized sparse autoencoder is performed on the input and mapped feature of UBL in order to maintain the intrinsic manifold structure of origin HSI. Then, the objective function of UBL composed of an $l_{2}$ -norm of output-layer weights and a graph regularization term is designed, which can be easily solved by choosing eigenvectors corresponding to the smallest eigenvalues. Finally, the HSI clustering results can be obtained by applying spectral clustering on the output of UBL. Experiments on three popular real HSI data sets demonstrate that, compared with several competitive methods, UBL can achieve better clustering performance.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Efficient Probabilistic Collaborative Representation-Based Classifier for
           Hyperspectral Image Classification
    • Authors: Yan Xu;Qian Du;Wei Li;Nicolas H. Younan;
      Pages: 1746 - 1750
      Abstract: This letter presents an efficient probabilistic collaborative representation-based classifier (PROCRC) for hyperspectral image classification. Its performance is evaluated on different types of spatial features of hyperspectral imagery (HSI) including shape feature (i.e., extended multiattribute feature), global feature (i.e., Gabor feature), and local feature [i.e., local binary pattern (LBP)]. Compared with the original collaborative representation classifier (CRC), the proposed PROCRC offers superior classification performance. The Tikhonov regularized versions of CRC have excellent classification performance but their computational cost is high. The experimental results show that the PROCRC can yield comparable classification accuracy but with much lower computational cost.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Hyperspectral Image Classification Using Random Occlusion Data
           Augmentation
    • Authors: Juan Mario Haut;Mercedes E. Paoletti;Javier Plaza;Antonio Plaza;Jun Li;
      Pages: 1751 - 1755
      Abstract: Convolutional neural networks (CNNs) have become a powerful tool for remotely sensed hyperspectral image (HSI) classification due to their great generalization ability and high accuracy. However, owing to the huge amount of parameters that need to be learned and to the complex nature of HSI data itself, these approaches must deal with the important problem of overfitting, which can lead to inadequate generalization and loss of accuracy. In order to mitigate this problem, in this letter, we adopt random occlusion, a recently developed data augmentation (DA) method for training CNNs, in which the pixels of different rectangular spatial regions in the HSI are randomly occluded, generating training images with various levels of occlusion and reducing the risk of overfitting. Our results with two well-known HSIs reveal that the proposed method helps to achieve better classification accuracy with low computational cost.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Multi-task Joint Sparse and Low-rank Representation Target Detection for
           Hyperspectral Image
    • Authors: Xing Wu;Xia Zhang;Yi Cen;
      Pages: 1756 - 1760
      Abstract: Target detection plays an important role in hyperspectral imagery (HSI) processing. Many detection algorithms have been proposed over the past decades. However, the existing detectors may encounter false alarms for ignoring target interference during background modeling and high correlations among adjacent bands. To address the target interference issue, we propose a novel joint-sparse and low-rank representation target detection algorithm for HSI, which separately models target and background pixels using different regularization methods. A background pixel in HSI can be modeled via sparse and low-rank representation using a background dictionary, whereas a target pixel can be modeled via sparse representation using a target dictionary. To reduce spectral redundancy, we further incorporated the detection model into a multitask learning framework. The final detection was made in favor of the class with the lowest total reconstruction error accumulated from all tasks. Experiments on two airborne HSIs demonstrated that multitask joint-sparse and low-rank representation (MTJSLR) outperformed other state-of-the-art detectors.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Improved Faster R-CNN With Multiscale Feature Fusion and Homography
           Augmentation for Vehicle Detection in Remote Sensing Images
    • Authors: Hong Ji;Zhi Gao;Tiancan Mei;Yifan Li;
      Pages: 1761 - 1765
      Abstract: Vehicle detection in remote sensing images has attracted remarkable attention for its important role in a variety of applications in traffic, security, and military fields. Motivated by the stunning success of region convolutional neural network (R-CNN) techniques, which have achieved the state-of-the-art performance in object detection task on benchmark data sets, we propose to improve the Faster R-CNN method with better feature extraction, multiscale feature fusion, and homography data augmentation to realize vehicle detection in remote sensing images. Extensive experiments on representative remote sensing data sets related to vehicle detection demonstrate that our method achieves better performance than the state-of-the-art approaches. The source code will be made available (after the review process).
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • End-to-End DSM Fusion Networks for Semantic Segmentation in
           High-Resolution Aerial Images
    • Authors: Zhiying Cao;Kun Fu;Xiaode Lu;Wenhui Diao;Hao Sun;Menglong Yan;Hongfeng Yu;Xian Sun;
      Pages: 1766 - 1770
      Abstract: Semantic segmentation in high-resolution aerial images is a fundamental research problem in remote sensing field for its wide range of applications. However, it is difficult to distinguish regions with similar spectral features using only multispectral data. Recent research studies have indicated that the introduction of multisource information can effectively improve the robustness of segmentation method. In this letter, we use digital surface models (DSMs) information as a complementary feature to further improve the semantic segmentation results. To this end, we propose a lightweight and simple DSM fusion (DSMF) branch structure module. Compared with the existing feature extraction structures, proposed DSMF module is simple and can be easily applied to other networks. In addition, we investigate four fusion strategies based on DSMF module to explore the optimal feature fusion strategy and four end-to-end DSMFNets are designed according to the corresponding strategies. We evaluate our models on International Society for Photogrammetry and Remote Sensing Vaihingen data set and all DSMFNets achieve promising results. In particular, DSMFNet-1 achieves an overall accuracy of 91.5% on the test data set.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • An Approach to Tree Detection Based on the Fusion of Multitemporal LiDAR
           Data
    • Authors: Daniele Marinelli;Claudia Paris;Lorenzo Bruzzone;
      Pages: 1771 - 1775
      Abstract: The repetitive acquisition of airborne light detection and ranging (LiDAR) data for forest surveys is rapidly increasing, thus making possible the forest dynamic analysis. Moreover, the availability of multitemporal data enables the possibility to improve the forest attribute estimates performed at single date, especially when one LiDAR acquisition has a lower pulse density with respect to the other. This letter presents a novel approach that exploits the bitemporal data information to: 1) improve the tree detection at both dates and 2) identify forest changes at single tree level. This is done by using a novel compound approach to the detection of trees in bitemporal data based on the Bayes rule for minimum error. Significant geometric features are extracted for each candidate tree-top and are used to estimate statistical terms employed in the compound approach. The multitemporal information is considered by estimating (in an iterative way) the probabilities of transition, which takes into account the temporal dependence between the LiDAR acquisitions. The proposed approach is evaluated on multitemporal LiDAR data acquired in a coniferous forest located in the Southern Italian Alps. Experimental results confirm the effectiveness of the compound detection that increases the overall accuracy (OA) up to 8.6% with respect to the single-date detection.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Nonconvex Log-Sum Function-Based Majorization–Minimization Framework for
           Seismic Data Reconstruction
    • Authors: Wanjuan Zhang;Lihua Fu;Qun Liu;
      Pages: 1776 - 1780
      Abstract: Because of the fact that complete seismic data can have a low rank in the frequency-space (f-x) domain, rank-reduction methods are classical techniques used for seismic data reconstruction. Models that employ nuclear-norm minimization signify convex relaxation methods in traditional rank minimization problems. However, the results obtained after solving the nuclear-norm minimization problem are usually suboptimal because the nuclear norm indicates a loose approximation of the rank function. To overcome the limitations of the nuclear norm, we propose a new method of seismic data reconstruction based on the log-sum function minimization, which is closer to the rank function than the convex nuclear norm. However, the problem based on the log-sum function is nonconvex. Consequently, a majorization–minimization framework has been adopted to solve the associated minimization problem. Numerical experiments performed using synthetic and real data demonstrate that the quality of reconstruction derived from our proposed algorithm is better than that of the singular value thresholding algorithm and the weighted nuclear norm method.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Extreme Learning Machine-Based Heterogeneous Domain Adaptation for
           Classification of Hyperspectral Images
    • Authors: Li Zhou;Li Ma;
      Pages: 1781 - 1785
      Abstract: An extreme learning machine (ELM)-based heterogeneous domain adaptation (HDA) algorithm is proposed for the classification of remote sensing images. In the adaptive ELM network, one hidden layer is used for the source data to provide the random features, whereas two hidden layers are set for target data to produce the random features as well as a transformation matrix. DA is achieved by constraining both the source data and the transformed target data to share the same output weights. Moreover, manifold regularization is adopted to preserve the local geometry of unlabeled target data. The proposed ELM-based HDA (EHDA) method is applied to cross-domain classification of remote sensing images, and the experimental results using multisensor remote sensing images demonstrate the effectiveness of the proposed approach.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Discriminative Adaptation Regularization Framework-Based Transfer Learning
           for Ship Classification in SAR Images
    • Authors: Yongjie Xu;Haitao Lang;Lihui Niu;Chenguang Ge;
      Pages: 1786 - 1790
      Abstract: Ship classification in synthetic-aperture radar (SAR) images is of great significance for dealing with various marine matters. Although traditional supervised learning methods have recently achieved dramatic successes, but they are limited by the insufficient labeled training data. This letter presents a novel unsupervised domain adaptation (DA) method, termed as discriminative adaptation regularization framework-based transfer learning (D-ARTL), to address the problem in case that there is no labeled training data available at all in the SAR image domain, i.e., target domain (TD). D-ARTL improves the original ARTL by adding a novel source discriminative information preservation (SDIP) regularization term. This improvement achieves an efficient transfer of interclass discriminative ability from source domain (SD) to TD, while achieving the alignment of cross-domain distributions. Extensive experiments have verified that D-ARTL outperforms state-of-the-art methods on the task of ship classification in SAR images by transferring the automatic identification system (AIS) information.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • A Generalized Volume Scattering Model-Based Vegetation Index From
           Polarimetric SAR Data
    • Authors: Debanshu Ratha;Dipankar Mandal;Vineet Kumar;Heather Mcnairn;Avik Bhattacharya;Alejandro C. Frery;
      Pages: 1791 - 1795
      Abstract: In this letter, we propose a novel vegetation index from polarimetric synthetic-aperture radar (PolSAR) data using the generalized volume scattering model. The geodesic distance between two Kennaugh matrices projected on a unit sphere proposed by Ratha et al. is used in this letter. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and generalized volume scattering models. A factor is estimated corresponding to the ratio of the minimum to the maximum geodesic distances between the observed Kennaugh matrix and the set of elementary targets: trihedral, cylinder, dihedral, and narrow dihedral. This factor is then scaled and multiplied with the similarity measure to obtain the novel vegetation index. The proposed vegetation index is compared with the radar vegetation index (RVI) proposed by Kim and van Zyl. A time series of RADARSAT-2 data acquired during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) campaign in Manitoba, Canada, is used to assessing the proposed RVI.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Infrared and Visible Image Fusion Method by Using Hybrid Representation
           Learning
    • Authors: Guiqing He;Jiaqi Ji;Dandan Dong;Jun Wang;Jianping Fan;
      Pages: 1796 - 1800
      Abstract: For remote sensing image fusion, infrared and visible images have very different brightness due to their disparate imaging mechanisms, the result of which is that nontarget regions in the infrared image often affect the fusion of details in the visible image. This letter proposes a novel infrared and visible image fusion method basing hybrid representation learning by combining dictionary-learning-based joint sparse representation (JSR) and nonnegative sparse representation (NNSR). In the proposed method, different fusion strategies are adopted, respectively, for the mean image, which represents the primary energy information, and for the deaveraged image, which contains important detail features. Since the deaveraged image contains a large amount of high-frequency details information of the source image, JSR is utilized to sparsely and accurately extract the common and innovation features of the deaveraged image, thus, accurately merging high-frequency details in the deaveraged image. Then, the mean image represents low-frequency and overview features of the source image, according to NNSR, mean image is classified well-directed to different feature regions and then fused, respectively. Such proposed method, on the one hand, can eliminate the impact on fusion result suffering from very different brightness causing by different imaging mechanism between infrared and visible image; on the other hand, it can improve the readability and accuracy of the result fusion image. Experimental result shows that, compared with the classical and state-of-the-art fusion methods, the proposed method not only can accurately integrate the infrared target but also has rich background details of the visible image, and the fusion effect is superior.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • $k$+ NN+Search+for+Remote+Sensing+Image+Processing&rft.title=IEEE+Geoscience+and+Remote+Sensing+Letters&rft.issn=1545-598X&rft.date=2019&rft.volume=16&rft.spage=1801&rft.epage=1805&rft.aulast=Zhu;&rft.aufirst=Ying&rft.au=Ying+Zhong;Wei+Weng;Jianmin+Li;Shunzhi+Zhu;">Collaborative Cross-Domain $k$ NN Search for Remote Sensing Image
           Processing
    • Authors: Ying Zhong;Wei Weng;Jianmin Li;Shunzhi Zhu;
      Pages: 1801 - 1805
      Abstract: $k$ NN search is a fundamental function in image processing, which is useful in many real applications, including image cluster, image classification, and image understanding and analysis in general. In this light, we propose and study a novel collaborative cross-domain $k$ NN search (CD- $k$ NN) in multidomain space. Given a query location $q$ in a multidomain space (e.g., spatial domain, temporal domain, textual domain, and so on), the CD- $k$ NN finds top- $k$ data points with the minimum distance to $q$ . This problem is challenging due to two reasons. First, how to define practical distance measures to evaluate the distance in multidomain space. Second, how to prune the search space efficiently in multiple domains. To address the challenges, we define a linear combination method-based distance measure for multidomain space. Based on the distance measure, a collaborative search method is developed to constrain the CD search space in a comparable smaller range. A pair of upper and lower bounds is defined to prune the search space in multiple domains effectively. Finally, we conduct extensive experiments to verify that the developed methods can achieve a high performance.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
  • Introducing IEEE Collabratec
    • Pages: 1806 - 1806
      Abstract: Advertisement, IEEE. IEEE Collabratec is a new, integrated online community where IEEE members, researchers, authors, and technology professionals with similar fields of interest can network and collaborate, as well as create and manage content. Featuring a suite of powerful online networking and collaboration tools, IEEE Collabratec allows you to connect according to geographic location, technical interests, or career pursuits. You can also create and share a professional identity that showcases key accomplishments and participate in groups focused around mutual interests, actively learning from and contributing to knowledgeable communities. All in one place! Learn about IEEE Collabratec at ieeecollabratec.org.
      PubDate: Nov. 2019
      Issue No: Vol. 16, No. 11 (2019)
       
 
 
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