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

LENG Chengcai; HONG Yameng

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

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

更新时间：2026-01-02

- Application of an enhanced local self-similarity descriptor for multimodal remote sensing images matching
- Journal of Northwest University (Natural Science Edition) Vol. 55, Issue 6, Pages: 1253-1266(2025)
- 作者机构：
  
  西北大学　数学学院，陕西　西安　710127
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2025-06-005
  CLC： TP751.1
- Received：24 September 2025，
  
  Published：25 December 2025
- 稿件说明：
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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：

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.

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references

徐宇翔 , 胡庆武 , 段延松 , 等 . 融合LiDAR点云与无人机影像的滑坡动态监测技术 [J ] . 测绘通报 , 2024 ( 8 ): 42 - 47 .

XU Y X , HU Q W , DUAN Y S , et al . Landslide dynamic monitoring technology by integrating LiDAR point cloud and UAV image [J ] . Bulletin of Surveying and Mapping , 2024 ( 8 ): 42 - 47 .

朱柏 , 叶沅鑫 . 多模态遥感图像配准方法研究综述 [J ] . 中国图象图形学报 , 2024 , 29 ( 8 ): 2137 - 2161 .

ZHU B , YE Y X . Multimodal remote sensing image registration: A survey [J ] . Journal of Image and Graphics , 2024 , 29 ( 8 ): 2137 - 2161 .

樊仲藜 , 张力 , 王庆栋 , 等 . SAR影像和光学影像梯度方向加权的快速匹配方法 [J ] . 测绘学报 , 2021 , 50 ( 10 ): 1390 - 1403 .

FAN Z L , ZHANG L , WANG Q D , et al . A fast matching method of SAR and optical images using angular weighted orientated gradients [J ] . Acta Geodaetica et Cartographica Sinica , 2021 , 50 ( 10 ): 1390 - 1403 .

张浩 , 马佳义 , 樊凡 , 等 . 基于特征空间多分类对抗机制的红外与可见光图像融合 [J ] . 计算机研究与发展 , 2023 , 60 ( 3 ): 690 - 704 .

ZHANG H , MA J Y , FAN F , et al . Infrared and visible image fusion based on multiclassification adversarial mechanism in feature space [J ] . Journal of Computer Research and Development , 2023 , 60 ( 3 ): 690 - 704 .

彭韬 , 杨培臻 , 周亮 , 等 . 一种旋转和尺度不变的异源景象匹配方法 [J ] . 弹箭与制导学报 , 2024 , 44 ( 5 ): 38 - 46 .

PENG T , YANG P Z , ZHOU L , et al . A rotation and scale invariant heterogeneous sence matching approach [J ] . Journal of Projectiles, Rockets, Missiles and Guidance , 2024 , 44 ( 5 ): 38 - 46 .

孙彬 , 游航 , 李文博 , 等 . 双光载荷图像融合及其在低空遥感中的应用 [J ] . 航空学报 , 2025 , 46 ( 11 ): 58 - 81 .

SUN B , YOU H , LI W B , et al . Dual-band payload image fusion and its applications in low-altitude remote sensing [J ] . Acta Aeronautica et Astronautica Sinica , 2025 , 46 ( 11 ): 58 - 81 .

唐霖峰 , 张浩 , 徐涵 , 等 . 基于深度学习的图像融合方法综述 [J ] . 中国图象图形学报 , 2023 , 28 ( 1 ): 3 - 36 .

TANG L F , ZHANG H , XU H , et al . Deep learning-based image fusion: A survey [J ] . Journal of Image and Graphics , 28 ( 1 ): 3 - 36 .

YE Y X , TANG T F , ZHU B , et al . A multiscale framework with unsupervised learning for remote sensing image registration [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5622215 .

叶沅鑫 , 孙苗苗 , 王蒙蒙 , 等 . 结合邻域信息和结构特征的遥感影像变化检测 [J ] . 测绘学报 , 2021 , 50 ( 10 ): 1349 - 1357 .

YE Y X , SUN M M , WANG M M , et al . Change detection of remote sensing images by combining neighborhood information and structural features [J ] . Acta Geodaetica Cartography Sinica , 2021 , 50 ( 10 ): 1349 - 1357 .

叶沅鑫 , 王蒙蒙 , 杨超 , 等 . 基于方向相位稠密特征的多传感器遥感影像配准方法和系统 [J ] . 遥感学报 , 2024 , 28 ( 6 ): 1525 - 1538 .

YE Y X , WANG M M , YANG C , et al . A multi-sensor remote sensing registration method and system based on dense feature of orientated phase [J ] . National Remote Sensing Bulletin , 2022 , 28 ( 6 ): 1525 - 1538 .

XIANG Y M , WANG F , YOU H J . OS-SIFT: A robust SIFT-like algorithm for high-resolution optical-to-SAR image registration in suburban areas [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 6 ): 3078 - 3090 .

LOWE D G . Distinctive image features from scale-invariant keypoints [J ] . International Journal of Computer Vision , 2004 , 60 ( 2 ): 91 - 110 .

YE Y X , SHEN L , HAO M , et al . Robust optical-to-SAR image matching based on shape properties [J ] . IEEE Geoscience and Remote Sensing Letters , 2017 , 14 ( 4 ): 564 - 568 .

XIONG X , XU Q , JIN G W , et al . Rank-based local self-similarity descriptor for optical-to-SAR image matching [J ] . IEEE Geoscience and Remote Sensing Letters , 2020 , 17 ( 10 ): 1742 - 1746 .

LI J Y , HU Q W , AI M Y . RIFT: Multi-modal image matching based on radiation-variation insensitive feature transform [J ] . IEEE Transactions on Image Processing , 2020 , 29 : 3296 - 3310 .

HONG Y M , LENG C C , ZHANG X Y , et al . Max-index based local self-similarity descriptor for robust multi-modal image registration [J ] . IEEE Geoscience and Remote Sensing Letters , 2022 , 19 : 4505805 .

YAO Y X , ZHANG Y J , WAN Y , et al . Multi-modal remote sensing image matching considering co-occurrence filter [J ] . IEEE Transactions on Image Processing , 2022 , 31 : 2584 - 2597 .

LI J Y , HU Q W , ZHANG Y J . Multimodal image matching: A scale-invariant algorithm and an open dataset [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2023 , 204 : 77 - 88 .

SHECHTMAN E , IRANI M . Matching local self-similarities across images and videos [C ] // 2007 IEEE Conference on Computer Vision and Pattern Recognition . Minneapolis : IEEE , 2007 : 1 - 8 .

ROFFO G , MELZI S , CASTELLANI U , et al . Infinite feature selection: A graph-based feature filtering approach [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021 , 43 ( 12 ): 4396 - 4410 .

ROSTEN E , DRUMMOND T . Machine learning for high-speed corner detection [C ] // Proceedings of the 9th European Conference on Computer Vision . Graz : ACM , 2006 : 430 - 443 .

RUBLEE E , RABAUD V , KONOLIGE K , et al . ORB: An efficient alternative to SIFT or SURF [C ] // 2011 International Conference on Computer Vision . Barcelona : IEEE , 2012 : 2564 - 2571 .

HARRIS C G , STEPHENS M . A combined corner and edge detector [C ] // Proceedings ofthe Alvey Vision Conference . Manchester : Alvey Vision Club , 1988 : 1694378 .

LEUTENEGGER S , CHLI M , SIEGWART R Y . BRISK: Binary Robust invariant scalable keypoints [C ] // 2011 International Conference on Computer Vision . Barcelona : IEEE , 2012 : 2548 - 2555 .

XIANG Y M , TAO R S , WANG F , et al . Automatic registration of optical and SAR images via improved phase congruency model [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2020 , 13 : 5847 - 5861 .

HUANG M Y , XU Y , QIAN L X , et al . The QXS-SAROPT dataset for deep learning in SAR-optical data fusion [EB/OL ] . ( 2021-04-25 ) [ 2025-09-20 ] . https://arxiv.org/abs/2103.08259 https://arxiv.org/abs/2103.08259 .

LI J Y , XU W Y , SHI P C , et al . LNIFT: Locally normalized image for rotation invariant multimodal feature matching [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5621314 .

LI Y , LI B H , ZHANG G H , et al . sRIFD: A shift rotation invariant feature descriptor for multi-sensor image matching [J ] . Infrared Physics & Technology , 2023 , 135 : 104970 .

CAO F Z , SHI T X , HAN K Y , et al . RDFM: Robust deep feature matching for multimodal remote-sensing images [J ] . IEEE Geoscience and Remote Sensing Letters , 2023 , 20 : 6009605 .

GAO C Z , LI W , TAO R , et al . MS-HLMO: Multiscale histogram of local main orientation for remote sensing image registration [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5626714 .

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