The Experts below are selected from a list of 197502 Experts worldwide ranked by ideXlab platform
Wan Jian-wei - One of the best experts on this subject based on the ideXlab platform.
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A Review of Polarimetric SAR Image Classification
Signal Processing, 2008Co-Authors: Wan Jian-weiAbstract:A review of polarimetric SAR(synthetic aperture radar) Image Classification is provided.First,a summary development course of polarimetric SAR Image Classification is introduced,and primary research results are concluded.Second,many features,with their physical meanings and merits and deficiencies in pelarimetric Image Classification,are discussed.Third,many algorithms for super- vised and unsupervised Classification are summarized.Finally,existing problems and further research directions are pointed out.
Licheng Jiao - One of the best experts on this subject based on the ideXlab platform.
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Adversarial Reconstruction-Classification Networks for PolSAR Image Classification
Remote Sensing, 2019Co-Authors: Yanqiao Chen, Licheng Jiao, Cheng Peng, Xiangrong Zhang, Ronghua ShangAbstract:Polarimetric synthetic aperture radar (PolSAR) Image Classification has become more and more widely used in recent years. It is well known that PolSAR Image Classification is a dense prediction problem. The recently proposed fully convolutional networks (FCN) model, which is very good at dealing with the dense prediction problem, has great potential in resolving the task of PolSAR Image Classification. Nevertheless, for FCN, there are some problems to solve in PolSAR Image Classification. Fortunately, Li et al. proposed the sliding window fully convolutional networks (SFCN) model to tackle the problems of FCN in PolSAR Image Classification. However, only when the labeled training sample is sufficient, can SFCN achieve good Classification results. To address the above mentioned problem, we propose adversarial reconstruction-Classification networks (ARCN), which is based on SFCN and introduces reconstruction-Classification networks (RCN) and adversarial training. The merit of our method is threefold: (i) A single composite representation that encodes information for supervised Image Classification and unsupervised Image reconstruction can be constructed; (ii) By introducing adversarial training, the higher-order inconsistencies between the true Image and reconstructed Image can be detected and revised. Our method can achieve impressive performance in PolSAR Image Classification with fewer labeled training samples. We have validated its performance by comparing it against several state-of-the-art methods. Experimental results obtained by classifying three PolSAR Images demonstrate the efficiency of the proposed method.
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A Novel Deep Fully Convolutional Network for PolSAR Image Classification
Remote Sensing, 2018Co-Authors: Yanqiao Chen, Guangyuan Liu, Licheng JiaoAbstract:Polarimetric synthetic aperture radar (PolSAR) Image Classification has become more and more popular in recent years. As we all know, PolSAR Image Classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR Image Classification. However, there are some problems to be solved in PolSAR Image Classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR Image Classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the Image integrity can be maintained in the maximum extent. We use three PolSAR Images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR Image Classification.
Yanqiao Chen - One of the best experts on this subject based on the ideXlab platform.
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Adversarial Reconstruction-Classification Networks for PolSAR Image Classification
Remote Sensing, 2019Co-Authors: Yanqiao Chen, Licheng Jiao, Cheng Peng, Xiangrong Zhang, Ronghua ShangAbstract:Polarimetric synthetic aperture radar (PolSAR) Image Classification has become more and more widely used in recent years. It is well known that PolSAR Image Classification is a dense prediction problem. The recently proposed fully convolutional networks (FCN) model, which is very good at dealing with the dense prediction problem, has great potential in resolving the task of PolSAR Image Classification. Nevertheless, for FCN, there are some problems to solve in PolSAR Image Classification. Fortunately, Li et al. proposed the sliding window fully convolutional networks (SFCN) model to tackle the problems of FCN in PolSAR Image Classification. However, only when the labeled training sample is sufficient, can SFCN achieve good Classification results. To address the above mentioned problem, we propose adversarial reconstruction-Classification networks (ARCN), which is based on SFCN and introduces reconstruction-Classification networks (RCN) and adversarial training. The merit of our method is threefold: (i) A single composite representation that encodes information for supervised Image Classification and unsupervised Image reconstruction can be constructed; (ii) By introducing adversarial training, the higher-order inconsistencies between the true Image and reconstructed Image can be detected and revised. Our method can achieve impressive performance in PolSAR Image Classification with fewer labeled training samples. We have validated its performance by comparing it against several state-of-the-art methods. Experimental results obtained by classifying three PolSAR Images demonstrate the efficiency of the proposed method.
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A Novel Deep Fully Convolutional Network for PolSAR Image Classification
Remote Sensing, 2018Co-Authors: Yanqiao Chen, Guangyuan Liu, Licheng JiaoAbstract:Polarimetric synthetic aperture radar (PolSAR) Image Classification has become more and more popular in recent years. As we all know, PolSAR Image Classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR Image Classification. However, there are some problems to be solved in PolSAR Image Classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR Image Classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the Image integrity can be maintained in the maximum extent. We use three PolSAR Images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR Image Classification.
Philip J. Howarth - One of the best experts on this subject based on the ideXlab platform.
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Modeling errors in remote sensing Image Classification
Remote Sensing of Environment, 1993Co-Authors: Minhua Wang, Philip J. HowarthAbstract:Abstract Standard error assessment techniques in Image Classification have been primarily concerned with identifying errors in individual pixel assignments. However, these techniques overlook a fundamental fact that Image Classification is basically a process of generalization. The outputs of this process are often intended to be cartographic objects (e.g., polygons) which are abstract models of reality and may not be verifiable at each pixel. Linking errors with cartographic objects in Image Classification is a challenging problem in remote sensing. This article proposes a new error assessment methodology for Image Classification (an error model) in which uncertainties involved in the Classification process are estimated through simulations of various steps in Image Classification. Two error models have been developed to estimate the uncertainties involved in class modeling (training) and boundary generation (boundary pixel allocation). Results derived from two case studies show the validity of the proposed error concept for Image Classification and its potential for improving Image Classification.
Ronghua Shang - One of the best experts on this subject based on the ideXlab platform.
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Adversarial Reconstruction-Classification Networks for PolSAR Image Classification
Remote Sensing, 2019Co-Authors: Yanqiao Chen, Licheng Jiao, Cheng Peng, Xiangrong Zhang, Ronghua ShangAbstract:Polarimetric synthetic aperture radar (PolSAR) Image Classification has become more and more widely used in recent years. It is well known that PolSAR Image Classification is a dense prediction problem. The recently proposed fully convolutional networks (FCN) model, which is very good at dealing with the dense prediction problem, has great potential in resolving the task of PolSAR Image Classification. Nevertheless, for FCN, there are some problems to solve in PolSAR Image Classification. Fortunately, Li et al. proposed the sliding window fully convolutional networks (SFCN) model to tackle the problems of FCN in PolSAR Image Classification. However, only when the labeled training sample is sufficient, can SFCN achieve good Classification results. To address the above mentioned problem, we propose adversarial reconstruction-Classification networks (ARCN), which is based on SFCN and introduces reconstruction-Classification networks (RCN) and adversarial training. The merit of our method is threefold: (i) A single composite representation that encodes information for supervised Image Classification and unsupervised Image reconstruction can be constructed; (ii) By introducing adversarial training, the higher-order inconsistencies between the true Image and reconstructed Image can be detected and revised. Our method can achieve impressive performance in PolSAR Image Classification with fewer labeled training samples. We have validated its performance by comparing it against several state-of-the-art methods. Experimental results obtained by classifying three PolSAR Images demonstrate the efficiency of the proposed method.
