Please use this identifier to cite or link to this item: https://doi.org/10.3906/elk-1006-533
Title: Change detection without difference image computation based on multiobjective cost function optimization
Authors: Çelik, T. 
Yetgin, Z.
Keywords: Change detection
Environmental monitoring
Genetic algorithm
Image processing
Multiobjective cost functionkwd image quality metric
Optimization
Remote sensing
Satellite images
Issue Date: 2011
Citation: Çelik, T., Yetgin, Z. (2011). Change detection without difference image computation based on multiobjective cost function optimization. Turkish Journal of Electrical Engineering and Computer Sciences 19 (6) : 941-956. ScholarBank@NUS Repository. https://doi.org/10.3906/elk-1006-533
Abstract: In this paper, we propose a novel method for unsupervised change detection in multi-temporal satellite images by using multiobjective cost function optimization via genetic algorithm (GA). The spatial image grid of the input multi-temporal satellite images is divided into two distinct regions, representing "changed" and "unchanged" regions between input images, via the intermediate change detection mask produced by the GA. The dissimilarity of pixels of "changed" regions and similarity of pixels of "unchanged" regions between input multi-temporal images are measured usingimage quality metrics which consider correlation, spectral distortion, radiometric distortion, and contrast distortion. The contextual information of each pixel in intermediate change detection mask is considered by employing binary information around its 3 × 3neighborhood. A multiobjective cost function for the intermediate change detection mask is obtained by considering contextual information, similarity and dissimilarity measures. The final change detection mask is achieved through minimization of the multiobjective cost function via different realizations of intermediate change detection masks produced bythe GA. The proposed method does not need to compute difference image from multi-temporalsatellite images, thus can be used as a general purpose change detection method for both synthetic aperture radar (SAR) and optical images. Change detection results and comparisons with the state-of-the-art techniques are shown on multi-temporal georeferenced SAR images acquired by ESA ERS-2 on the city of San Francisco, California, and optical images acquired by Landsat 5 TM on part of Alaska. © TÜBITAK.
Source Title: Turkish Journal of Electrical Engineering and Computer Sciences
URI: http://scholarbank.nus.edu.sg/handle/10635/75713
ISSN: 13000632
DOI: 10.3906/elk-1006-533
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