IEEE Trans Image Process - Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

Tópicos

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Resumo

This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

Resumo Limpo

paper present unsupervis distributionfre chang detect approach synthet apertur radar sar imag base imag fusion strategi novel fuzzi cluster algorithm imag fusion techniqu introduc generat differ imag use complementari inform meanratio imag logratio imag order restrain background inform enhanc inform chang region fuse differ imag wavelet fusion rule base averag oper minimum local area energi chosen fuse wavelet coeffici lowfrequ band highfrequ band respect reformul fuzzi localinform cmean cluster algorithm propos classifi chang unchang region fuse differ imag incorpor inform spatial context novel fuzzi way purpos enhanc chang inform reduc effect speckl nois experi real sar imag show imag fusion strategi integr advantag logratio oper meanratio oper gain better perform chang detect result obtain improv fuzzi cluster algorithm exhibit lower error preexist

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