Different remote sensing image matching methods based on multiple constraints
Bai1(), Yuying2, Chengling3, Yanru2, Shihu1
1. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China 2. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083,China 3. Beijing GEOWAY Software Co., Ltd., Beijing 100043, China
In order to solve the problem that there are large geometric deformation and gray difference between different remote sensing satellite image and it is difficult to match a large number of feature points, the authors put forward a multi-source remote sensing image matching method under multiple constraints in this paper. First, ASIFT algorithm is used to extract high-quality feature points and complete the initial matching, and the matching results are optimized by RANSAC algorithm. Secondly, affine transformation matrix of the two images is calculated by using the matching feature points set, and the remaining feature points are matched again by combining affine transformation and gray correlation coefficient. Finally, support vector regression (SVR) is used to check the matching results. Satellite images of ZY3-01, ZY3-02 and GF-1 were selected in the experiment. The experimental results show that, compared with SIFT and ASIFT algorithms, the proposed method can greatly increase the number of matching points between multi-source remote sensing images and improve the matching accuracy.
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