Decision Fusion at Pixel Level of Multi-Band Data for Land Cover Classification-A Review. [PDF]
J ImagingPapadopoulos S +2 more
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Remote sensing based forest cover classification using machine learning. [PDF]
Sci RepAziz G +4 more
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Quantitative Retrieval of Soil Salinity in Arid Regions: A Radar Feature Space Approach with Fully Polarimetric SAR Data. [PDF]
Sensors (Basel)Nurmemet I +5 more
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Forest canopy closure estimation in mountainous southwest China using multi-source remote sensing data. [PDF]
Front Plant SciZhou W +7 more
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PolSAR Image Classification Based on Relation Network With SWANet
Remote Sensing, 2023 Deep learning and convolutional neural networks (CNN) have been widely applied in polarimetric synthetic aperture radar (PolSAR) image classification, and satisfactory results have been obtained. However, there is one crucial issue that still has not been solved. These methods require abundant labeled samples and obtaining the labeled samples of PolSAR
Hua Wenqiang, Wenqiang Hua, Xiaomin Jin
exaly +4 more sources
Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network
Remote Sensing, 2019 Although complex-valued (CV) neural networks have shown better classification results compared to their real-valued (RV) counterparts for polarimetric synthetic aperture radar (PolSAR) classification, the extension of pixel-level RV networks to the ...
Yan Wu, Peng Zhang
exaly +2 more sources
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Riemannian sparse coding for classification of PolSAR images
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016Hermitian positive definite (HPD) covariance matrices form one of the most widely-used data representations in PolSAR applications. However, most of these applications either use statistical distribution models on the PolSAR covariance matrices or polarimetric target decomposition. In this paper, we study HPD matrices for PolSAR image classification in
Wen Yang 0001 +3 more
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Exploring Convolutional Lstm for Polsar Image Classification
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important applications in Pol-SAR image processing. More and more deep learning methods are applied to PolSAR image classification. As we know, the polarimetric response of a target is related to the orientation of the target, but the features in rotation domain are ...
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Classification-oriented hyperspectral and PolSAR images synergic processing
2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013Classification is one of the most important applications in the field of remote sensing. How to improve the accuracy of classification is the critical topic that has long obsessed the researchers. In this paper, a fusion method based on a synergic use of hyperspectral data and Polarimetric SAR (PolSAR) data is presented.
Tong Li 0010 +3 more
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A new method for classification of POLSAR images
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), 2016The present paper proposes an unsupervised feature learning method for POLSAR image classification. The proposed method includes two steps. In these two steps, features are created and learned from scratch. The first is to learn dictionaries and encode features using scatter matrices.
Nastaran Aghaei, Gholamreza Akbarizadeh
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