4.7 Article

Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering

期刊

IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 21, 期 4, 页码 2141-2151

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2011.2170702

关键词

Clustering; fuzzy C-means (FCM) algorithm; image change detection; image fusion; synthetic aperture radar (SAR)

资金

  1. National High Technology Research and Development Program of China [2009AA12Z210]
  2. Program for New Century Excellent Talents in University [NCET-08-0811]
  3. Program for New Scientific and Technological Star of Shaanxi Province [2010KJXX-03]
  4. Fundamental Research Funds for the Central Universities [K50510020001]

向作者/读者索取更多资源

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.

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