多重约束条件下的不同遥感影像匹配方法

Different remote sensing image matching methods based on multiple constraints

针对不同遥感卫星影像间存在较大的几何变形和灰度差异,导致难以匹配大量的特征点问题,提出了一种多重约束条件下的不同遥感影像匹配方法。首先,利用仿射尺度不变特征变换(affine scale invariant feature transform,ASIFT)算法提取高质量的特征点完成初始匹配,并通过随机采样一致性算法优化匹配结果;其次,利用匹配特征点集合计算出两幅影像的仿射变换矩阵,结合仿射变换与灰度相关系数对剩余特征点进行再次匹配;最后,通过支持向量回归(support vector regression,SVR)对匹配结果进行检核。选取资源三号01星(ZY3-01)、资源三号02星(ZY3-02)以及高分一号(GF-1)卫星影像进行实验,结果表明,相较于尺度不变特征变换与ASIFT算法,本方法可以大量增加不同遥感影像间的特征点匹配数目,提高匹配精度。

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.