Change detection with one-class sparse representation classifier

Qiong Ran; Mengmeng Zhang; Wei Li; Qian Du

doi:10.1117/1.JRS.10.042006

6 September 2016 Change detection with one-class sparse representation classifier

Qiong Ran, Mengmeng Zhang, Wei Li, Qian Du

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 4, 042006 (September 2016). https://doi.org/10.1117/1.JRS.10.042006

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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Qiong Ran, Mengmeng Zhang, Wei Li, and Qian Du "Change detection with one-class sparse representation classifier," Journal of Applied Remote Sensing 10(4), 042006 (6 September 2016). https://doi.org/10.1117/1.JRS.10.042006

Published: 6 September 2016

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