A reliable matching algorithm for heterogeneous remote sensing images considering the spatial distribution of matched features

Owing to the differences in sensor types,resolutions, and imaging conditions of heterologous remote sensing images, thematching results of remote sensing images, such as low accuracy, few matched pairs,and low distribution quality, are not ideal, which makes precise registrationbetween heterogeneous images difficult. To mitigate this, we propose a reliablematching algorithm for heterogeneous remote sensing images that considers thespatial distribution of the matched features. First, feature-based matchingalgorithms such as the scale-invariant feature transform (SIFT) algorithm orthe speeded-up robust features algorithm are used to match images to obtain aninitial set of matched pairs and a set of candidate features. Then, accordingto the stability of the spatial distribution of locally correctly matchedfeatures, the distance and angular proximity between matched features and theirneighbours are calculated to obtain the accuracy of the matched pairs and removeincorrectly matched pairs. Finally, the random sample consensus (RANSAC)algorithm was used to fit the transformation model between images, and thefinal matched feature selection algorithm and automatic transformation erroralgorithm were used to detect candidate features to increase the number ofmatched pairs. Experimental analysis of heterogeneous multiscale andmultitemporal optical remote sensing images demonstrates the superiorcapability of the proposed algorithm over commonly used algorithms, includingSIFT, RANSAC, locality preserving matching, learning a two-class classifier formismatch removal, and linear adaptive filtering algorithms. In particular, whenthe precision of the initially matched pair is low, the proposed algorithm canachieve excellent results.