Results 1 to 10 of about 2,397 (207)
Adversarial Reconstruction-Classification Networks for PolSAR Image Classification [PDF]
Remote Sensing, 2019 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 (
Yanqiao Chen +5 more
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Structure Label Matrix Completion for PolSAR Image Classification [PDF]
Remote Sensing, 2020 Terrain classification is a hot topic in polarimetric synthetic aperture radar (PolSAR) image interpretation that aims at assigning a label to every pixel and forms a label matrix for a PolSAR image.
Qian Wu +5 more
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POLSAR Image Classification via Clustering-WAE Classification Model
IEEE Access, 2018 Considering the clustering algorithms could explore the label information automatically, this paper proposes a new method in terms of polarimetric synthetic aperture radar (POLSAR) image classification, which named a clustering-wishart-auto-encoder (WAE)
Wen Xie, Ziwei Xie, Feng Zhao, Bo Ren
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Joint Polarimetric-Adjacent Features Based on LCSR for PolSAR Image Classification [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Image classification is a critical and important application in PolSAR image interpretation. Finding a feature extraction method, which can effectively describe the characteristics of the target, is an important basis for image classification.
Xiao Wang +3 more
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Feature fusion method based on local binary graph for PolSAR image classification [PDF]
IET Radar, Sonar &Navigation, Volume 17, Issue 6, Page 939-954, June 2023., 2023 We propose a novel supervised version of GCNs named mini‐GCNs, for short. As the name suggests, mini‐GCNs can be trained in mini‐batch fashion, trying to achieve a better and more robust local optimum. To remove the redundant information extracted from the designed CNN and mini‐GCN, a feature fusion method is proposed to achieve better classification ...
Mohammad Ali Sebt, Mohsen Darvishnezhad
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Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification [PDF]
Remote Sensing, 2020 Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification.
Yuwei Guo +5 more
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POLSAR IMAGE CLASSIFICATION USING DIFFERENT CODIFICATIONS BASED ON FISHER VECTORS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Abstract. A PolSAR is an active sensing device capable of providing images that are robust against variations of weather and atmosphere conditions, irrespective of the time of the day they were acquired. For an efficient use of these images it is necessary to have algorithms capable of classifying these images to generate maps with their content ...
J. A. Redolfi +2 more
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CV-CPKAN: Complex-Valued Convolutional Kolmogorov–Arnold Framework for PolSAR Image Classification
Remote SensingDeep learning has significantly advanced PolSAR image processing, with a growing trend of integrating mathematical theories into deep neural networks to enhance their capabilities with regard to complex data.
Zuzheng Kuang +4 more
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Classification of PolSAR Images by Stacked Random Forests [PDF]
ISPRS International Journal of Geo-Information, 2018 This paper proposes the use of Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar images. SRF apply several Random Forest instances in a sequence where each individual uses the class estimate of its predecessor as an additional feature. To this aim, the internal node tests are designed to work not only directly
Ronny Hänsch, Olaf Hellwich
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHigh-quality labeled samples of polarimetric synthetic aperture radar (PolSAR) images are relatively scarce. Therefore, achieving optimal classification performance with limited labeled samples has become a significant challenge in PolSAR image ...
Nana Jiang +4 more
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