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Title: Level set incorporated with an improved MRF model for unsupervised change detection for satellite images
Authors: Zhang, X
Shi, W 
Hao, M
Shao, P
Lyu, X
Issue Date: 2017
Source: European journal of remote sensing, 2017, v. 50, no. 1, p. 202-210
Abstract: This study proposes the use of a level set incorporated with an improved Markov random field (MRF) model in unsupervised change detection for satellite images. MRF provides a means of modelling the spatial contextual information in the level set, and an edge indicator function is introduced into the MRF model to control the contribution of local information in the boundary areas to change detection. On the basis of the improved MRF model, local label relationships and edge information are considered in the level set energy functional to conduct a novel local term and attract the contours into desired objects. By merging the novel energy term, the proposed approach not only reduces noise but also obtains accurate outlines of the changed regions. Experimental results obtained with Landsat 7 Enhanced Thematic Mapper Plus and SPOT 5 data sets confirm the superiority of the proposed model when compared with state-of-the-art change detection methods.
Keywords: Level set
Markov random field
Change detection
Satellite images
Publisher: Associazione Italiana di Telerilevamento
Journal: European journal of remote sensing 
ISSN: 2279-7254
EISSN: 1129-8596
DOI: 10.1080/22797254.2017.1308236
Rights: © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Zhang, X., Shi, W., Hao, M., Shao, P., & Lyu, X. (2017). Level set incorporated with an improved MRF model for unsupervised change detection for satellite images. European Journal of Remote Sensing, 50(1), 202-210.is available at https://doi.org/10.1080/22797254.2017.1308236
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