SAR Images Matching Improvement in Radargrammetric Conditions

摘要

SAR (Synthetic Aperture Radar) image matching is the most critical step in a radargrammetric chain. The presence of speckle noise makes classical methods (used in optics) inefficient, and some specific techniques must be developed. In this paper, we firstly present the Alcatel's radargrammetric chain : the two images are first resampled in epipolar geometry, to reduce the space search of homologous points, then the matching results are optimized using a TABU search with a regularity constraint. Having the disparity map, we obtain the 3D information by a space triangulation. We also generate a confidence coefficient to determine the most robust disparities. This radargrammetric chain gives encouraging results, but the matching step still raises problems : the area based matching method suffers from speckle, and processing time is considerably increased by the optimization method. So we present secondly the new matching module we are working on, integrating feature based methods. To detect edges we use the ROEWA operator (Ratio Of Exponentially Weighted Average) which is well adapted to SAR images. Edges can be extracted by different ways : watershed algorithm or maximum tracking. The objective is to find the most robust edges in both stereoscopic images, in order to match them and create a first set of matched couples. This will guide our search in the generation of a dense disparity map. We finally propose the global SAR image matching module including edge extraction and matching, radiometric correlation, and eventually user control, to generate an accurate disparity map.