NDLI: A multiobjective optimization method based on MOEA/D and fuzzy clustering for change detection in SAR images

Content Provider	IEEE Xplore Digital Library
Author	Qiao Wang Hao Li Maoguo Gong Linzhi Su Licheng Jiao
Copyright Year	2014
Description	Author affiliation: Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi'an, China (Qiao Wang; Hao Li; Maoguo Gong; Linzhi Su; Licheng Jiao)
Abstract	For the presence of speckle noise in SAR images, many change detection methods have been developed to suppress the effect of noise. However, all these methods will result in the loss of image details, and the trade-off between detail preserving and noise removing capability has become an urgent problem remaining to be settled. In this paper, we put forward an innovation for change detection in synthetic aperture radar images. It integrates evolutionary computation into fuzzy clustering process, and considers detail preserving capability and noise removing capability as two separate objectives for multiobjective optimization, and thus transforming the change detection problem into a multiobjective optimization problem (MOP). Experiments conducted on real S AR images confirm that the new approach is efficient.
Starting Page	3024
Ending Page	3029
File Size	706314
Page Count	6
File Format	PDF
e-ISBN	9781479914883
DOI	10.1109/CEC.2014.6900269
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Synthetic aperture radar Linear programming Pareto optimization Speckle Evolutionary computation Pareto optimal solution Change detection fuzzy clusterin multiobjective optimization problem (MOP)
Content Type	Text
Resource Type	Article

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