8 March 2018 Polarimetric SAR image classification based on discriminative dictionary learning model
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Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106110D (2018) https://doi.org/10.1117/12.2284795
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.
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Cheng Wei Sang, Cheng Wei Sang, Hong Sun, Hong Sun, } "Polarimetric SAR image classification based on discriminative dictionary learning model", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106110D (8 March 2018); doi: 10.1117/12.2284795; https://doi.org/10.1117/12.2284795
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