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Before applying change detection, high resolution SAR (Synthetic Aperture Radar) imagery benefits from advanced denoising mechanisms to preserve details and minimize speckle. We propose a change detector based on a MCA (Morphological Components Analysis) of a difference image (DI). With MCA, the data is decomposed into image features utilizing sparse representations of the image content. By introducing <i>a priori</i> knowledge of the content of the scenes, and exploiting shape information corresponding to the changes provided by MCA, we can significantly improve performance under adverse conditions, such as inconsistent acquisition geometries.