Systems Engineering and Electronics ›› 2018, Vol. 40 ›› Issue (3): 692-698.doi: 10.3969/j.issn.1001-506X.2018.03.31

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Joint sparse representation of hyperspectral image classification based on kernel function

CHEN Shanxue, ZHOU Yanfa, QI Ruolan   

  1. Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Online:2018-02-26 Published:2018-02-26

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Abstract:

In order to make full use of the similarity and uniqueness of hyperspectral image neighborhood pixel, a joint sparse representation classification (K-JSRC) method based on kernel function is proposed to improve the classification accuracy of the hyperspectral image. The method uses an improved kernel function to measure the similarity of the neighborhood of the measured cell to the neighborhood pixel by adapting all the neighborhood pixels of each neighborhood of the measured cell to obtain the irregular and excellent neighborhood window. The experimental results on the two hyperspectral data sets of Indian Pines and University of Pavia show that the proposed classification algorithm has a good classification of hyperspectral images and its classification accuracy is superior to the similar algorithms.

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