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Proceedings Paper

Sparse representation and smooth filtering for hyperspectral image classification
Author(s): Mengmeng Zhang; Qiong Ran; Wei Li; Kui Liu
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Paper Abstract

Sparse representation-based classification (SRC) has gained great interest recently. A pixel to be classified is sparsely approximately by labeled samples, and it is assigned to the class whose labeled samples provide the smallest representation error. In this paper, we extend SRC by exploiting the benefits of using a smoothing filter based on sparse gradient minimization. The smoothing filter is expected to provide less intra class variability and more spatial regularity, which eliminating the inherent variations within a small neighborhood. Classification performance on two real hyperspectral datasets demonstrates that our proposed method has improved classification accuracy and the resulting accuracies are persistently higher at all small training sample size situations compared to some traditional classifiers.

Paper Details

Date Published: 9 December 2015
PDF: 13 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98083P (9 December 2015); doi: 10.1117/12.2205325
Show Author Affiliations
Mengmeng Zhang, Beijing Univ. of Chemical Technology (China)
Qiong Ran, Beijing Univ. of Chemical Technology (China)
Wei Li, Beijing Univ. of Chemical Technology (China)
Kui Liu, Intelligent Fusion Technology (United States)


Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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