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Feature extraction and scene classification for remote sensing image based on sparse representation
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Paper Abstract

Sparse representation theory for classification is an active research area. Signals can potentially have a compact representation as a linear combination of atoms in an overcomplete dictionary. In this paper, a novel classification method is proposed, which combines sparse-representation-based classification (SRC) and K-nearest neighbor classifier for remote sensing image. Based on the extracted multidimensional features which are used to constitute an overcomplete dictionary, the image is expressed as the product of the dictionary and coefficient of sparse representation. Then the test image is reconstructed by utilizing correlation and distance information between the image and each class simultaneously. Finally, each image will be assigned a class label based on minimizing the reconstruction error. And then, the proposed method has been extended to a kernelized variant to solve linearly inseparable problems. The experimental results show that the proposed method and its variant not only improve the classification performance over SRC but also outperform typical classifiers, such as support vector machine(SVM), especially when the number of training samples is limited.

Paper Details

Date Published: 14 May 2019
PDF: 8 pages
Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109861U (14 May 2019); doi: 10.1117/12.2518337
Show Author Affiliations
Youliang Guo, Harbin Institute of Technology (China)
Junping Zhang, Harbin Institute of Technology (China)
Shengwei Zhong, Harbin Institute of Technology (China)


Published in SPIE Proceedings Vol. 10986:
Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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