自然资源遥感2025，Vol.37Issue(2)：19-29,11.DOI:10.6046/zrzyyg.2023333
基于宽度学习的非监督SAR影像变化检测
Unsupervised change detection using SAR images based on the broad learning system
摘要
Abstract
Change detection using synthetic aperture radar(SAR)images based on deep learning has been a significant research topic in the field of remote sensing.However,it is limited by unreliable training samples and highly time-consuming training.Hence,this study proposed a novel unsupervised change detection method using SAR images based on the broad learning system(BLS).First,a reliable pre-classification method is presented by incorporating neighborhood information into similarity operators,adaptive dual-threshold segmentation,superpixel correction,and visual saliency analysis.This pre-classification method generates a pre-classification map and corresponding training samples.Second,the BLS network is trained using the training samples to generate the BLS-based prediction map for change detection.Third,the pre-classification map and the BLS-based prediction map are fused through two-stage voting to generate the final change detection map.The experimental results of five real SAR image datasets show that the proposed method can produce more reliable training samples and achieve higher accuracy in change detection.Moreover,its efficiency is significantly higher than the change detection model using SAR images based on deep learning.关键词
非监督变化检测/合成孔径雷达/宽度学习/自适应双阈值/超像素/视觉显著性分析Key words
unsupervised change detection/synthetic aperture radar/broad learning system/adaptive dual thresh-old/superpixel/visual saliency analysis分类
计算机与自动化引用本文复制引用
邵攀,管宗胜,贾付文..基于宽度学习的非监督SAR影像变化检测[J].自然资源遥感,2025,37(2):19-29,11.基金项目
国家自然科学基金项目"模糊拓扑空间下高分辨率遥感影像多尺度融合变化检测方法研究"(编号:41901341)和湖北省自然科学基金一般面上项目"集成局部和全局特性的通道深度交互融合建筑物提取方法研究"(编号:2024AFB867)共同资助. （编号:41901341）
