Fractal genetic model in change detection of SAR images

Hossein Aghababaei*, Jalal Amini, Y.C. Tzeng, Josaphat Tetuko Sri Sumantyo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


The paper presents an effective way of detecting the changes of multi-temporal synthetic aperture radar (SAR) images. An accurate unsupervised change detection method that combines the intensity information and the fractal dimension of SAR images is proposed based on the fractal genetic model (FGM). The model computes firstly the local fractal dimension of the SAR images to obtain the fractal image and next a new proposed measure (D) is calculated from the normalized ratio of SAR images and the normalized difference of fractal images. Finally, the change map is derived by minimizing a cost function using a genetic algorithm (GA) on the derived image from the measure. Experimental results of detecting changes from SAR images acquired by ASAR on board ENVISAT and ALOS-PALSAR reveal that the proposed method is an effective and efficient tool for change detection from SAR images.
Original languageEnglish
Pages (from-to)739-747
Number of pages9
JournalPhotonirvachak = Journal of the Indian society of remote sensing
Publication statusPublished - 2013
Externally publishedYes




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