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。
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.
作者
冷成财
洪雅萌
LENG Chengcai;HONG Yameng(School of Mathematics,Northwest University,Xi'an 710127,China)
出处
《西北大学学报(自然科学版)》
北大核心
2025年第6期1253-1266,共14页
Journal of Northwest University(Natural Science Edition)
基金
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)。
关键词
多模态遥感图像匹配
局部自相似性
特征选择
multimodal remote sensing image matching
local self-similarity
feature selection
