An improved Normalized Cross Correlation algorithm for SAR image registration

This paper proposes a robust and fast matching method based on Normalized Cross Correlation (NCC) for Synthetic Aperture Radar (SAR) image matching. NCC is a robust algorithm in SAR image matching. Two main drawbacks of the NCC algorithm are the flatness of the similarity measure maxima, due to the self-similarity of the images, and the high computational complexity [1]. To tackle these two problems, we adopt the block partitioning strategy, texture feature analysis, and the Fast Fourier Transformation (FFT) algorithm and Integral Images to improve the performance of the conventional NCC algorithm. In the block partitioning strategy, we divide the template and the corresponding sub-window in the examined image into some sub-blocks, and there are several sub-blocks in the template, then we use texture features to increase the weight of sub-blocks which contain more terrain information in the template during the matching process, in this way we improve the flatness of the similarity measure maxima greatly. After that we use the FFT algorithm and Integral Images to speed up the proposed method, with the actual situation of our experiment we adopt the FFT and Integral Images based on the block partitioning strategy, thus we significantly reduce the number of computations required to carry out template matching based on the conventional NCC. Experimental results show that the proposed algorithm is more robust and faster than the conventional NCC algorithm.