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Mathematical Problems in Engineering
Volume 2018, Article ID 8264961, 10 pages
Research Article

Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images

1The School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
2The School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China

Correspondence should be addressed to Aizhong Mi; nc.ude.uph@gnohziaim

Received 26 May 2018; Revised 15 August 2018; Accepted 23 August 2018; Published 2 October 2018

Academic Editor: Stefano Sfarra

Copyright © 2018 Haifeng Sima et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification method is investigated by flexible patches sampling of superpixels. First, the principal component analysis and total variation diffusion are employed to form the pseudo color image for simplifying superpixels computing with (simple linear iterative clustering) SLIC model. Then, we design a joint sparse recovery model by sampling overcomplete patches of superpixels to estimate joint sparse characteristics of test pixel, which are carried out on the orthogonal matching pursuit (OMP) algorithm. At last, the pixel is labeled according to the minimum distance constraint for final classification based on the joint sparse coefficients and structured dictionary. Experiments conducted on two real hyperspectral datasets show the superiority and effectiveness of the proposed method.