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

ABSTRACT

Owing to the differences in sensor types, resolutions, and imaging conditions of heterologous remote sensing images, the matching results of remote sensing images, such as low accuracy, few matched pairs, and low distribution quality, are not ideal, which makes precise registration between heterogeneous images difficult. To mitigate this, we propose a reliable matching algorithm for heterogeneous remote sensing images that considers the spatial distribution of the matched features. First, feature-based matching algorithms such as the scale-invariant feature transform (SIFT) algorithm or the speeded-up robust features algorithm are used to match images to obtain an initial set of matched pairs and a set of candidate features. Then, according to the stability of the spatial distribution of locally correctly matched features, the distance and angular proximity between matched features and their neighbours are calculated to obtain the accuracy of the matched pairs and remove incorrectly matched pairs. Finally, the random sample consensus (RANSAC) algorithm was used to fit the transformation model between images, and the final matched feature selection algorithm and automatic transformation error algorithm were used to detect candidate features to increase the number of matched pairs. Experimental analysis of heterogeneous multiscale and multitemporal optical remote sensing images demonstrates the superior capability of the proposed algorithm over commonly used algorithms, including SIFT, RANSAC, locality preserving matching, learning a two-class classifier for mismatch removal, and linear adaptive filtering algorithms. In particular, when the precision of the initially matched pair is low, the proposed algorithm can achieve excellent results.

KEYWORDS:

• Heterogeneous remote sensing images
• image matching
• proximity
• similarity measure

Acknowledgements

The authors give their appreciations to the Geospatial Data Cloud for providing Landsat-8 OLI data and ASF Data Search for providing AVNIR-2 data.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42271342]; Natural Science Foundation of Jiangsu Province [BK20201372]; National Natural Science Foundation of China [41631175]; National Natural Science Foundation of China [42071364]