Remote Sensing Image Automatic Registration on Multi-scale Harris-Laplacian

In order to overcome the difficulty of automatic image registration in image preprocessing, this paper presents an automatic registration algorithm for remote sensing images with different spatial resolutions. The algorithm is studied based on Harris-Laplacian corner detection, which can determine the affine transformation (zoom, rotation, translation) between images of different scales. The corners in the reference and registration images are firstly detected and located by a multi-scale Harris-Laplacian (H-L) corner detector. Secondly, the algorithm chooses SURF (Speeded Up Robust Feature) descriptor to calculate the detected corners descriptors. Then, the multi-resolution corner matching is achieved based on Euclid distance. Finally, according to the LoG (Laplacian Of Gaussian), the scale factor is automatically determined between reference and registration images. A number of remote sensing images are tested, and the experiments show that the studied algorithm can register two remote sensing images of different sizes and resolutions automatically. It also verifies that the algorithm has the lower time cost comparing with the other existing algorithms (e.g. SIFT) within certain detecting accuracy level. This algorithm is also useful for resolving the problem of potential errors due to parallax effects when establishing geometric affine transformation on corners for detecting on buildings with different unknown elevations.