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Change Detection in SAR Images Based On NSCT and Spatial Fuzzy Clustering Approach

Krishnakumar P, Y.Ramesh Babu

The project presents change detection approach for synthetic aperture radar (SAR) images based on an image fusion and a spatial fuzzy clustering algorithm. The image fusion technique will be introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. NSCT (Non- sub sampled contourlet transform) fusion rules based on an average operator and minimum local area energy are chosen to fuse the contourlet coefficients for a lowfrequency band and a high-frequency band, respectively to restrain the background information and enhance the information of changed regions in the fused difference image. A fuzzy local-information Cmeans clustering algorithm will be 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. For the remote sensing images, differencing (subtraction operator) and rationing (ratio operator) are wellknown techniques for producing a difference image. In differencing, changes are measured by subtracting the intensity values pixel by pixel between the considered couple of temporal images. In rationing, changes are obtained by applying a pixel-by-pixel ratio operator to the considered couple of temporal images. In the case of SAR images, the ratio operator is typically used instead of the subtraction operator since the image differencing technique is not adapted to the statistics of SAR images. The results will be proven that rationing generates better difference image for change detection using spatial fuzzy clustering approach and efficiency of this algorithm will be exhibited by sensitivity and correlation evaluation.