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

冷成财; 洪雅萌

doi:10.16152/j.cnki.xdxbzr.2025-06-005

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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

青年编委专刊 | 更新时间：2026-01-02

- 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
- Application of an enhanced local self-similarity descriptor for multimodal remote sensing images matching
- 西北大学学报（自然科学版） 2025年55卷第6期页码：1253-1266
- 作者机构：
  
  西北大学　数学学院，陕西　西安　710127
- 作者简介：
  
  [ "冷成财，博士（后），西北大学数学学院教授，统计系主任，博士生导师。主要从事数据处理与统计分析、计算机视觉与模式识别等研究，主持国家自然科学基金、中国博士后基金等10余项。以第一或通讯作者及合作身份发表学术论文80余篇，包含IEEE TIP、IEEE TNNLS等多篇顶级论文，其中SCI收录40余篇。担任Computers and Electrical Engineering（2018）和Remote Sensing（2021—2023）客座编辑。中国人工智能学会科普委员，中国人工智能学会青工委员会委员，全国工业统计学教学研究会理事。2017年入选西北大学“青年学术英才支持计划”，2018年获陕西高等学校科学技术奖一等奖（第三完成人），2019年获陕西省科学技术奖二等奖（第四完成人），2023年和2025年被评为西北大学优秀硕士学位论文指导老师，2025年度获陕西高等学校科学技术研究优秀成果奖二等奖（第一完成人）。" ]
- 基金信息：
  
  国家自然科学基金数学天元基金(12326612);陕西省自然科学基金(2025JC-YBMS-772);中国国家留学基金管理委员会国家公派奖学金项目(202406970047)
- DOI：10.16152/j.cnki.xdxbzr.2025-06-005
  中图分类号： TP751.1
- 收稿：2025-09-24，
  
  纸质出版：2025-12-25
- 稿件说明：
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冷成财, 洪雅萌. 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[J]. 西北大学学报（自然科学版）, 2025,55(6):1253-1266.

LENG Chengcai, HONG Yameng. Application of an enhanced local self-similarity descriptor for multimodal remote sensing images matching[J]. Journal of Northwest University (Natural Science Edition), 2025, 55(6): 1253-1266.
冷成财, 洪雅萌. 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[J]. 西北大学学报（自然科学版）, 2025,55(6):1253-1266. DOI： 10.16152/j.cnki.xdxbzr.2025-06-005.

LENG Chengcai, HONG Yameng. Application of an enhanced local self-similarity descriptor for multimodal remote sensing images matching[J]. Journal of Northwest University (Natural Science Edition), 2025, 55(6): 1253-1266. DOI： 10.16152/j.cnki.xdxbzr.2025-06-005.

摘要

多模态遥感图像之间存在复杂差异与多样内容，导致匹配任务难以精确完成。尽管传统的局部自相似性（local self-similarity，LSS）描述符能够捕捉图像中的显著结构与轮廓，但由于差异复杂，往往难以实现精确的特征对应。为此，在描述符构建过程中引入基于图模型的特征筛选方法，节点集和边集分别表示局部单元内不同角度的LSS特征及其相似性关系。通过记录相关性排序索引并生成排序频率直方图，对LSS进行重构，仅保留最相关的特征，从而降低对强度信息的依赖。实验在3个公开数据集（含4类跨模态场景，250张测试图像）中验证有效性。定量分析表明，结合4类典型特征检测器后，ELSS（enhanced LSS）最高可获得276.92的NCM值和2.08的RMSE值，优于其他6种对比方法，证明了ELSS能有效缓解辐射和几何差异对多模态图像任务的限制，验证了其在多模态图像精准匹配任务下的适用性。

Abstract

Multi-modal remote sensing images exhibit complex discrepancies and diverse contents

posing significant challenges 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 (encompassing four cross-modal scenarios with 250 test images) validate the effectiveness of our enhanced LSS. When combined with four representative feature detectors

ELSS achieves state-of-the-art 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 effectiveness in mitigating radiometric and geometric variations for robust multimodal image matching.

关键词

Keywords

references

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