A local descriptor based registration method for multispectral remote sensing images with non-linear intensity differences
Image registration is a crucial step for remote sensing image processing. Automatic registration of multispectral remote sensing images could be challenging due to the significant non-linear intensity differences caused by radiometric variations among such images. To address this problem, this paper proposes a local descriptor based registration method for multispectral remote sensing images. The proposed method includes a two-stage process: pre-registration and fine registration. The pre-registration is achieved using the Scale Restriction Scale Invariant Feature Transform (SR-SIFT) to eliminate the obvious translation, rotation, and scale differences between the reference and the sensed image. In the fine registration stage, the evenly distributed interest points are first extracted in the pre-registered image using the Harris corner detector. Then, we integrate the local self-similarity (LSS) descriptor as a new similarity metric to detect the tie points between the reference and the pre-registered image, followed by a global consistency check to remove matching blunders. Finally, image registration is achieved using a piecewise linear transform. The proposed method has been evaluated with three pairs of multispectral remote sensing images from TM, ETM+, ASTER, Worldview, and Quickbird sensors. The experimental results demonstrate that the proposed method can achieve reliable registration outcome, and the LSS-based similarity metric is robust to non-linear intensity differences among multispectral remote sensing images.
multispectral remote sensing image image registration sr-sift local self-similarity