Abstract: Aiming at the problem that the position scale orientation-scale invariant feature transform (PSO-SIFT) algorithm was difficult to find enough correct corresponding relations for oilfield remote sensing images in the case of obvious differences in gray levels, and it took a long time, we proposed an
image matching algorithm based on improved PSO-SIFT algorithm. Firstly, we adopted the idea of “backing” character block to construct feature descriptors, which reduced the dimension of the feature descriptors. Secondly, we used a matching strategy that combined the bilateral functions for global motion modeling (BF) algorithm and the fast sample consensus (FSC) algorithm to eliminate mismatches from the obtained matching pairs and increase the number of correct matches. Finally, we compared the proposed algorithm with four similar algorithms and the original PSO-SIFT algorithm. The experimental results show that the proposed algorithm is more accurate than similar algorithms. Compared with the original algorithm, the proposed algorithm not only guarantees the accuracy of image matching, but also increases the number of correct matching pairs by about three times, and shortens the matching time by about 20 s.

Key words: information processing technology, position scale orientation-scale invariant feature transform (PSO-SIFT) algorithm, image matching;
 “backing” character descriptor, bilateral functions for global motion modeling (BF) algorithm, fast sample consensus (FSC) algorithm