Automatic Registration Algorithm for SAR and Optical Images Based on Shearlet and Sparse Representation

The registration of synthetic aperture radar (SAR) and optical images is still a challenging task due to the influence of the potential nonlinear intensity differences and severe speckle noise. In this letter, we propose a feature-based SAR and optical image registration algorithm combining shearlet and sparse representation. The work consists of three main components, including the feature points detector, descriptor, and matching criterion. First, a new feature points detection method based on the coherence-enhancing diffusion Harris detector (CED-HD) is designed, it integrates a coherence-enhancing diffusion function and shearlet-based spatial constraint on the basis of the Harris-Laplace detector, which can obtain highly repeatable feature points while suppressing speckle noise. Second, a multilayer complementary joint representation descriptor (MCJRD) based on shallow structural features and deep semantic features is designed. The shallow structural features are obtained jointly by shearlet and phase congruence, while the deep semantic features are obtained by multilayer convolution sparse representation, which makes the descriptors more discriminable and robust. Finally, a feature matching criterion based on locally constrained sparse representation is designed to better reduce the reconstruction error and improve the matching ability. Experimental results on several real SAR and optical image pairs demonstrate the effectiveness of the proposed algorithm.