Research Papers

Joint sparse hyperspectral image classification based on adaptive spatial context

[+] Author Affiliations
Yang Xu

Nanjing University of Science and Technology, School of Computer Science and Technology, Nanjing 210094, China

Zebin Wu

Nanjing University of Science and Technology, School of Computer Science and Technology, Nanjing 210094, China

Jiangsu Key Lab of Spectral Imaging and Intelligent Sensing, Nanjing 210094, China

Zhihui Wei

Nanjing University of Science and Technology, School of Computer Science and Technology, Nanjing 210094, China

J. Appl. Remote Sens. 8(1), 083552 (Sep 10, 2014). doi:10.1117/1.JRS.8.083552
History: Received March 25, 2014; Revised August 4, 2014; Accepted August 13, 2014
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Abstract.  Hyperspectral image (HSI) analysis is attracting a growing interest in real-world applications, many of which can finally be transformed into classification tasks. Traditional spectral-spatial HSI classification methods take advantage of the identical spatial information that is available everywhere, but this is not always the case, especially in the class boundary. A method for HSI classification based on the spectral information and the adaptive spatial context is proposed. First, we introduce a high-dimensional steering kernel to describe the adaptive spatial context and select the spatial correlative pixels of a given test pixel according to the adaptive spatial context. The selected pixels can be simultaneously sparse represented by linear combinations of a few common training samples. Then, a classifier imposing the adaptive spatial context to determine the final label of the test pixel is proposed. Experimental results on real HSIs show that our algorithm outperforms other state-of-art algorithms.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Yang Xu ; Zebin Wu and Zhihui Wei
"Joint sparse hyperspectral image classification based on adaptive spatial context", J. Appl. Remote Sens. 8(1), 083552 (Sep 10, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083552


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