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Journal of Applied Remote Sensing

Classification of hyperspectral urban data using adaptive simultaneous orthogonal matching pursuit
Author(s): Jinyi Zou; Wei Li; Xin Huang; Qian Du
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Paper Abstract

Simultaneous orthogonal matching pursuit (SOMP) has been recently developed for hyperspectral image classification. It utilizes a joint sparsity model with the assumption that each pixel can be represented by a linear combination of labeled samples. We present an approach to improve the performance of SOMP based on <italic<a priori</italic< segmentation map. According to the map, we first build a local region where within-segment pixels are preserved while between-segment pixels are excluded. Hyperspectral pixels in the preserved region around the test pixel are then simultaneously represented by a linear combination of training samples, whose weights are recovered by solving a sparsity-constrained optimization problem. Finally, the label of the test pixel is determined to be the class that yields the minimal total residuals between the test samples and the approximations. Experimental results demonstrate that the proposed adaptive SOMP (ASOMP) is superior to some existing classifiers, such as the original SOMP and the recently proposed weighted-SOMP (WSOMP). For example, the ASOMP performed with an accuracy of 95.53% for the ROSIS University of Pavia data with 120 training samples per class, while SOMP obtained an accuracy of 87.61%, an improvement of approximately 8%.

Paper Details

Date Published: 18 July 2014
PDF: 14 pages
J. Appl. Remote Sens. 8(1) 085099 doi: 10.1117/1.JRS.8.085099
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Jinyi Zou, Beijing Univ. of Chemical Technology (China)
Wei Li, Beijing Univ. of Chemical Technology (China)
Xin Huang, Wuhan Univ. (China)
Qian Du, Mississippi State Univ. (United States)

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