A Remote Sensing Image Matching Method Combining Genetic Algorithm with Least Square Matching

Image matching is an important task in digital photogrammetry. In the paper, an approach to remote sensing image matching combining genetic algorithm(GA) with least square matching(LSM) is presented to speed up image matching and provide a robust reliable and rather accurate initial value for high-precision subpixel matching. The experiment show that the matching method based on GA is much faster than those based on Sequential Similarity Detection Algorithm(SSDA) and classical Mean Absolute Difference(MAD). Taking our experiment data as an example, the rate of the sum of correlation calculation of MAD to that of GA is at least 34:1, this is to say, the speed of the later is thirtyfour times faster than that of the former theoretically.