A new affine invariant descriptor framework in shearlets domain for SAR image multiscale registration
To cope with the problem of accurate synthetic aperture radar (SAR) image registration, a new affine invariant descriptor framework in shearlets domain is proposed. This framework consists of three steps in sequence. First, a new affine invariant descriptor is developed based on scale invariant features transform (SIFT) and kernel space theory, which is called kernel affine invariant SIFT (KA-SIFT). Then the new descriptors are used to match the feature points detected from the different sub-images in the corresponding layer, which are obtained by the shearlet decomposition and affine-SIFT (ASIFT) algorithm. Finally, a coarse-to-fine procedure is adopted for gradual optimizing transformation parameters to achieve the multiscale registration. Experimental results show that this framework is more robust and accurate than some state-of-the-art methods. It can accurately detect the changes of the reservoirs and lakes before and after the Wenchuan earthquake, validating the proposed framework.
shearlets sar image image registration scale invariant feature transform