Hyperspectral image classification based on spatial and spectral features and sparse representation

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• 作者：Jing-Hui Yang (1)
  Li-Guo Wang (1)
  Jin-Xi Qian (2) (3)
  
  1. College of Information and Communication Engineering ; Harbin Engineering University ; Harbin ; 150001 ; China
  2. Institute of Telecommunication Satellites ; China Academy of Space Technology ; Beijing ; 100094 ; China
  3. School of Electronic Engineering ; Beijing University of Posts and Telecommunications ; Beijing ; 100876 ; China
  
• 关键词：Hyperspectral ; classification ; sparse representation ; spatial features ; spectral features
• 刊名：Applied Geophysics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：11
• 期：4
• 页码：489-499
• 全文大小：820 KB
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• 刊物主题：Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences;
• 出版者：Springer Berlin Heidelberg
• ISSN：1993-0658

文摘

To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method (Gabor-NWSF and SRC), abbreviated GNWSF-SRC. The proposed (GNWSF-SRC) method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.