Special Section on Airborne Hyperspectral Remote Sensing of Urban Environments

Classification of hyperspectral urban data using adaptive simultaneous orthogonal matching pursuit

[+] Author Affiliations
Jinyi Zou

Beijing University of Chemical Technology, College of Information Science and Technology, Beijing 100029, China

Wei Li

Beijing University of Chemical Technology, College of Information Science and Technology, Beijing 100029, China

Xin Huang

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China

Qian Du

Mississippi State University, Department of Electrical and Computer Engineering, Mississippi 39762, United States

J. Appl. Remote Sens. 8(1), 085099 (Jul 18, 2014). doi:10.1117/1.JRS.8.085099
History: Received March 22, 2014; Revised June 6, 2014; Accepted June 11, 2014
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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 a priori 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%.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Jinyi Zou ; Wei Li ; Xin Huang and Qian Du
"Classification of hyperspectral urban data using adaptive simultaneous orthogonal matching pursuit", J. Appl. Remote Sens. 8(1), 085099 (Jul 18, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.085099


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