A Geometry-Aware Registration Algorithm for Multiview High-Resolution SAR Images

Despite impressive progress in the past decade, accurate and efficient multiview synthetic aperture radar (SAR) image registration remains a challenging task due to complex imaging mechanisms and various imaging conditions. Especially, for rugged areas, SAR images obtained from the opposite-side view reflect different characteristics, making popular SAR image registration methods no longer applicable. To this end, we propose a geometry-aware image registration method by extracting inherent orientation features and concentrating on geometry-invariant areas. First, slant range images are terrain-corrected using a digital elevation model (DEM) to reduce large relative positioning errors caused by elevation. Second, the Gabor-ratio detector is introduced to obtain multiscale orientation features, which are more robust under various imaging conditions. Then, a geometry-aware mask is produced by intersecting the 3-D space ray with DEM, and thus, SAR images can be divided into three categories, layover, shadow, and geometry-invariant areas. The geometry-aware matching method, which focuses on geometry-invariant areas and masks out misleading caused by geometric and radiometric distortions, is proposed to realize accurate matching. The rational polynomial coefficients (RPCs) are refined to achieve relative correction. Extensive results on dozens of SAR images demonstrate the effectiveness and universality of the proposed algorithm by quantitative evaluation using man-made and natural corner reflectors. An analysis of the factors affecting registration accuracy is also discussed.