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Locality Regularized Robust-PCRC: A Novel Simultaneous Feature Extraction and Classification Framework for Hyperspectral Images

Yang, Zhijing; Cao, Faxian; Cheng, Yongqiang; Ling, Wing-Kuen; Hu, Ruo


Zhijing Yang

Faxian Cao

Yongqiang Cheng

Wing-Kuen Ling

Ruo Hu


Despite the successful applications of probabilistic collaborative representation classification (PCRC) in pattern classification, it still suffers from two challenges when being applied on hyperspectral images (HSIs) classification: 1) ineffective feature extraction in HSIs under noisy situation; and 2) lack of prior information for HSIs classification. To tackle the first problem existed in PCRC, we impose the sparse representation to PCRC, i.e., to replace the 2-norm with 1-norm for effective feature extraction under noisy condition. In order to utilize the prior information in HSIs, we first introduce the Euclidean distance (ED) between the training samples and the testing samples for the PCRC to improve the performance of PCRC. Then, we bring the coordinate information (CI) of the HSIs into the proposed model, which finally leads to the proposed locality regularized robust PCRC (LRR-PCRC). Experimental results show the proposed LRR-PCRC outperformed PCRC and other state-of-the-art pattern recognition and machine learning algorithms.


Yang, Z., Cao, F., Cheng, Y., Ling, W., & Hu, R. (in press). Locality Regularized Robust-PCRC: A Novel Simultaneous Feature Extraction and Classification Framework for Hyperspectral Images. IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, 1-16.

Journal Article Type Article
Acceptance Date Apr 14, 2020
Online Publication Date May 8, 2020
Deposit Date Jul 9, 2020
Publicly Available Date Jul 10, 2020
Journal IEEE Transactions on Geoscience and Remote Sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Pages 1-16
Keywords Coordinate information (CI); Euclidean distance (ED); Hyperspectral image (HSIs); Probabilistic collaborative representation classification (PCRC); Sparse representation
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