Abstract
It is very difficult to perform change detection in SAR images, because speckle noise contaminates the images in nature. Speckle, which results from coherent energy imaging, is indeed a chaotic phenomenon. As a result, an SAR signal can be modeled by a spatial chaotic system and characterized by its fractal dimension. The differential box-counting (DBC) technique is adopted to estimate fractal dimension in this paper. Based on the spatial chaotic model (SCM), a simplified SAR image change detection procedure is proposed. Observations provided by SAR sensors are uncertain due to changing illumination conditions at different acquiring time. Besides, the selection of window size M and grid size s in DBC provides an additional degree of uncertainty. Both the uncertainty involved in the measurements and the uncertainty involved in the selection of M and s motivate us of integrating type-2 fuzzy sets with the SCM to achieve a better performance. The proposed approach is applied to multitemporal polarimetric SAR images for change detections as demonstrations. The change detection results of using the original SCM method and the proposed approach are compared. The effects of misregistration for different change detection approaches are also presented. Simulation results suggest that the proposed approach is more tolerant to misregistration and offers better results of detecting changes when speckle noise is present.
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