FRIM: A Framework for Satellite-Borne Remote Sensing Image Matching

Satellite-borne remote sensing images cover extensive areas, and improvements in sensor resolution have dramatically increased the data volume of individual images. For example, a GF7 forward-looking image can span 31267×30996 pixels, with slight variations across datasets. To overcome the incompatibility of general-purpose image matching algorithms with large-scale satellite data, this study proposes a unified framework for adapting these algorithms to satellite-borne remote sensing images. The framework integrates two partitioning strategies—object-space-based and image-space-based—to balance computational efficiency and the uniform distribution of homologous points. Furthermore, an affine local aggregation strategy further enhances matching accuracy and spatial uniformity, achieving an 84.8% improvement in accuracy and a 97.2% enhancement in uniformity. Building on this, the dynamic recursive outlier elimination (DROE) method iteratively optimizes matching, increasing the precision of retained points by 14.5% and their uniformity by 89.5%, while effectively rejecting outliers. Experimental validation on multisource datasets confirms the framework’s ability to accurately match homologous points using general-purpose image matching algorithms, supporting both sparse and dense output requirements. The proposed framework will be publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Geng-Zemin/RS-matching-framework</uri>