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Journal of Applied Remote Sensing

Change detection with one-class sparse representation classifier
Author(s): Qiong Ran; Mengmeng Zhang; Wei Li; Qian Du
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Paper 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.

Paper Details

Date Published: 6 September 2016
PDF: 10 pages
J. Appl. Rem. Sens. 10(4) 042006 doi: 10.1117/1.JRS.10.042006
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Qiong Ran, Beijing Univ. of Chemical Technology (China)
Mengmeng Zhang, Beijing Univ. of Chemical Technology (China)
Wei Li, Beijing Univ. of Chemical Technology (China)
Qian Du, Mississippi State Univ. (United States)

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