加强局部自相似性描述符在多模态遥感图像匹配中的应用

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中图分类号：TP751.1 DOI：10. 16152/j. cnki. xdxbzr. 2025-06-005

Application of an enhanced local self-similarity descriptor for multimodal remote sensing images matching

LENG Chengcai， HONG Yameng （SchoolofMathematics，NorthwestUniversity，Xi’an 71O127，China）

AbstractMulti-modal remote sensing images exhibit complex discrepancies and diverse contents，posing significant challnges for precise matching tasks. While traditional local self-similarity （LSS）descriptors can capture salient structures and contours，their ability to establish accurate feature correspondences remains limited due to these intricate variations.To address this，we propose an enhanced LSS（ELSS）descriptor incorporating graph-based feature selection during descriptor construction.The method models LSS features at different angles within local patches as graph nodes，with edges representing their similarity relationships.By recording correlation ranking indices and generating ranking frequency histograms， we reconstruct LSS to retain only the most relevant features，thereby reducing its dependency on intensity information.Experiments on three public datasets （encompassng four cross-modal scenarios with 25O test images）validate the effectiveness of our enhanced LSS.When combined with four representative feature detectors，ELSS achieves state-of-theart results： 276.92 for number correct matches（NCM） and 2.08 for root mean square error （RMSE），outperforming six competing methods. These results validate ELSS’s efectiveness in mitigating radiometric and geometric variations for robust multimodal image matching.

Keywordsmultimodal remote sensing image matching; local self-similarity； feature selection

图像匹配在航空与环境监测、自主导航、机器人定位和测绘等领域具有重要应用价值[]，并在图像处理与计算机视觉研究中受到广泛关注。（剩余18336字）