Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76575
Title: Level set evolution with local uncertainty constraints for unsupervised change detection
Authors: Zhang, XK
Shi, WZ 
Liang, P
Hao, M
Issue Date: 2017
Publisher: Taylor & Francis
Source: Remote sensing letters, 2017, v. 8, no. 8, p. 811-820 How to cite?
Journal: Remote sensing letters 
Abstract: This letter presents a novel method for unsupervised change detection (CD) from remote sensing images using level set evolution with local uncertainty constraints (LSELUC). Uncertainty analysis of pixel labels was implemented as prior information to guide the evolution of level curves. Then, local uncertainty and gradient information of level curves were incorporated into the level set energy function to construct local energy constraints. The proposed method can reduce noise, while preserving details in change regions. Furthermore, an advanced regularization strategy of the level set function was adopted to improve the computational efficiency. The performance of the proposed method was validated on two remote sensing data sets. Experimental results show that the proposed method can produce satisfactory CD results.
URI: http://hdl.handle.net/10397/76575
ISSN: 2150-704X
EISSN: 2150-7058
DOI: 10.1080/2150704X.2017.1317929
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