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Journal of Applied Remote Sensing

Joint sparse hyperspectral image classification based on adaptive spatial context
Author(s): Yang Xu; Zebin Wu; Zhi-Hui Wei
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Paper 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.

Paper Details

Date Published: 10 September 2014
PDF: 13 pages
J. Appl. Remote Sens. 8(1) 083552 doi: 10.1117/1.JRS.8.083552
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Yang Xu, Nanjing Univ. of Science and Technology (China)
Zebin Wu, Nanjing Univ. of Science and Technology (China)
Jiangsu Key Lab. of Spectral Imaging and Intelligent Sensing (China)
Zhi-Hui Wei, Nanjing Univ. of Science and Technology (China)


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