Results 11 to 20 of about 3,590 (264)
AIR-PolSAR-Seg: A Large-Scale Data Set for Terrain Segmentation in Complex-Scene PolSAR Images
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a fundamental research topic in PolSAR image interpretation. Recently, many studies have been investigated to handle this task.
Zhirui Wang +4 more
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Speeding up Non-Gaussian POLSAR image analysis [PDF]
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017 Non-Gaussian statistical models fit SAR data better than Gaussian-based statistics, in most cases, but are complicated and time-consuming to use for unsupervised image segmentation via probabilistic clustering. The more advanced the model, the more complicated and slow the clustering.
Doulgeris, Anthony Paul, Hu, Dingsheng
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Remote Sensing, 2022 Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is ...
Wen Xie, Licheng Jiao, Wenqiang Hua
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Fisher Vectors for PolSAR Image Classification [PDF]
IEEE Geoscience and Remote Sensing Letters, 2017 In this letter, we study the application of the Fisher vector (FV) to the problem of pixelwise supervised classification of polarimetric synthetic aperture radar images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real parts of these matrices preserve the positive
Javier Redolfi +2 more
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DNN-Based PolSAR Image Classification on Noisy Labels
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Deep neural networks (DNNs) appear to be a solution for the classification of polarimetric synthetic aperture radar (PolSAR) data in that they outperform classical supervised classifiers under the condition of sufficient training samples. The design of a classifier is challenging because DNNs can easily overfit due to limited remote sensing training ...
Jun Ni +5 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Convolutional neural network (CNN) has achieved remarkable success in polarimetric synthetic aperture radar (PolSAR) image classification. However, the PolSAR image classification is a pixelwise prediction assignment.
Feng Zhao +3 more
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Earth and Space Science, Volume 10, Issue 6, June 2023., 2023 Abstract The polarimetric Synthetic Aperture Radar (SAR) data sets have been widely exploited for land use land cover (LULC) classification due to their sensitivity to the structural and dielectric properties of the imaging target. In this study, the potential of fully polarimetric L‐ and S‐band Airborne SAR (LS‐ASAR) data sets were explored for the ...
Shatakshi Verma +2 more
wiley +1 more source
Feature fusion method based on local binary graph for PolSAR image classification
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
wiley +1 more source
PolSAR Image Classification via Learned Superpixels and QCNN Integrating Color Features
Remote Sensing, 2019 Polarimetric synthetic aperture radar (PolSAR) image classification plays an important role in various PolSAR image application. And many pixel-wise, region-based classification methods have been proposed for PolSAR images.
Xinzheng Zhang +4 more
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DESPECKLING POLSAR IMAGES BASED ON RELATIVE TOTAL VARIATION MODEL [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 Relatively total variation (RTV) algorithm, which can effectively decompose structure information and texture in image, is employed in extracting main structures of the image.
C. Jiang +6 more
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