Fully polarimetric high-resolution airborne SAR image change detection with morphological component analysis

Abstract

Change detection for high resolution Synthetic Aperture Radar (SAR) imagery requires advanced denoising mechanisms to preserve details and minimize speckle. In this work, we propose a change detector based on a Morphological Component Analysis (MCA) of the scattering mechanisms provided with fully polarimetric data sets. With MCA, the power of each scattering mechanism is decomposed into diverse image features. By introducing a priori knowledge of the content of the scenes, and exploiting both the scattering mechanisms and their corresponding shapes, we can significantly improve performance, with fewer false alarms introduced by clutter, focusing errors, and inconsistent acquisition geometries. ABSTRACTChange detection for high resolution Synthetic Aperture Radar (SAR) imagery requires advanced denoising mechanisms to preserve details and minimize speckle. In this work, we propose a change detector based on a Morphological Component Analysis (MCA) of the scattering mechanisms provided with fully polarimetric data sets. With MCA, the power of each scattering mechanism is decomposed into diverse image features. By introducing a priori knowledge of the content of the scenes, and exploiting both the scattering mechanisms and their corresponding shapes, we can significantly improve performance, with fewer false alarms introduced by clutter, focusing errors, and inconsistent acquisition geometries.