Special Section on Sparsity-Driven High Dimensional Remote Sensing Image Processing and Analysis

Change detection with one-class sparse representation classifier

[+] Author Affiliations
Qiong Ran, Mengmeng Zhang, Wei Li

Beijing University of Chemical Technology, College of Information Science and Technology, North Third Ring Road 15, Chaoyang District, Beijing 100029, China

Qian Du

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

J. Appl. Remote Sens. 10(4), 042006 (Sep 06, 2016). doi:10.1117/1.JRS.10.042006
History: Received January 14, 2016; Accepted August 4, 2016
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Abstract.  A one-class sparse representation classifier (OCSRC) is proposed to solve the multitemporal change detection problem for identifying disaster affected areas. The OCSRC method, which is adapted from a sparse representation classifier (SRC), incorporates the one-class strategy from a one-class support vector machine (OCSVM) to seek accurate representation for the class of changed areas. It assumes that pixels from the changed areas can be well represented by samples from this class, thus the representation errors are taken as the possibilities of change. Performances of OCSRC and OCSVM are tested and compared with multitemporal multispectral HJ-1A images acquired in Heilongjiang Province before and after the flood in 2013. The entire image, together with two subimages, are used for overall comparison and detailed discussion. Receiver-operating-characteristics curve results show that OCSRC outperforms OCSVM by a lower false-positive rate at a defined true-positive rate (TPR), and the gap is more obvious with high TPR values. The same outcome is also manifested in the change detection image results, with less misclassified pixels for OCSRC at certain TPR values, which implies a more accurate description of the changed area.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Qiong Ran ; Mengmeng Zhang ; Wei Li and Qian Du
"Change detection with one-class sparse representation classifier", J. Appl. Remote Sens. 10(4), 042006 (Sep 06, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.042006


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