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Low-complexity multiple collaborative representations for hyperspectral image classification
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Paper Abstract

Collaborative representation has been a popular classifier for hyperspectral image classification because it can offer excellent classification accuracy with a closed-form solution. Collaborative representation can be implemented using a dictionary with training samples of all-classes, or using class-specific sub-dictionaries. In either case, a testing pixel is assigned to the class whose training samples offer the minimum representation residual. The Collaborative Representation Optimized Classifier with Tikhonov regularization (CROCT) was developed to combine these two types of collaborative representations to achieve the balance for optimized performance. The class-specific collaborative representation involves inverse operation of matrices constructed from class-specific samples, and the all-class version requires inversion operation of the matrix constructed from all samples. In this paper, we propose a low-complexity CROCT to avoid redundant operations in all-class and class-specific collaborative representations. It can further reduce the computational cost of CROCT while maintaining its excellent classification performance.

Paper Details

Date Published: 5 October 2017
PDF: 7 pages
Proc. SPIE 10430, High-Performance Computing in Geoscience and Remote Sensing VII, 1043003 (5 October 2017); doi: 10.1117/12.2278841
Show Author Affiliations
Yan Xu, Mississippi State Univ. (United States)
Qian Du, Mississippi State Univ. (United States)
Wei Li, Beijing Univ. of Chemical Technology (China)
Nicolas H. Younan, Mississippi State Univ. (United States)


Published in SPIE Proceedings Vol. 10430:
High-Performance Computing in Geoscience and Remote Sensing VII
Bormin Huang; Sebastián López; Zhensen Wu, Editor(s)

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