基于改进PSO-SIFT算法的油田遥感图像匹配

Oilfield Remote Sensing Image Matching Based on Improved PSO-SIFT Algorithm

针对油田遥感图像在灰度有明显差异的情况下,联合位置、尺度和方向的尺度不变特征变换(PSO-SIFT)算法很难为其找到足够多的正确对应关系,且花费时间较长的问题,提出一种基于改进PSO-SIFT算法的图像匹配算法.首先采用“回”字型分块思想构建特征描述符,降低特征描述子的维度;然后使用基于全局运动建模的双边函数(BF)算法与快速样本共识(FSC)算法相结合的匹配策略,对所得的匹配对进行误匹配剔除,以增加正确匹配的数量;最后将该算法与4种同类算法及原PSO-SIFT算法进行对比.实验结果表明,该算法比同类算法精度更高,与原算法相比不仅保证了图像匹配的精度,正确匹配对数量也增加了约3倍,且匹配时间约缩短20 s.

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)al gorithm and the fast sample consensus(FSC)algorithm to eliminate mismatches from the obtained matching pairs and increase the number of correct matches.Final ly,we compared the proposed algorithm with four similar algorithms and the original PSO-SIFT algorithm.The experimental results show that the propos ed 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.