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Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification

Hubei Key Laboratory of Applied Mathematics, Faculty of Mathematics and Statistics, Hubei University, Wuhan 430062, China
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Remote Sens. 2019, 11(9), 1114; https://doi.org/10.3390/rs11091114
Received: 15 March 2019 / Revised: 24 April 2019 / Accepted: 5 May 2019 / Published: 9 May 2019
By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to the presence of noisy or inhomogeneous pixels around the central testing pixel in the spatial domain, the performance of KJSR is greatly affected. Motivated by the idea of self-paced learning (SPL), this paper proposes a self-paced KJSR (SPKJSR) model to adaptively learn weights and sparse coefficient vectors for different neighboring pixels in the kernel-based feature space. SPL strateges can learn a weight to indicate the difficulty of feature pixels within a spatial neighborhood. By assigning small weights for unimportant or complex pixels, the negative effect of inhomogeneous or noisy neighboring pixels can be suppressed. Hence, SPKJSR is usually much more robust. Experimental results on Indian Pines and Salinas hyperspectral data sets demonstrate that SPKJSR is much more effective than traditional JSR and KJSR models. View Full-Text
Keywords: hyperspectral image classification; self-paced learning; kernel; joint sparse representation hyperspectral image classification; self-paced learning; kernel; joint sparse representation
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Hu, S.; Peng, J.; Fu, Y.; Li, L. Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification. Remote Sens. 2019, 11, 1114.

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