自然资源遥感2025，Vol.37Issue(5)：131-140,10.DOI:10.6046/zrzyyg.2024267
基于上下文敏感贝叶斯网络的角度阈值多元变化检测
Multivariate alteration detection using angle thresholds based on a context-sensitive Bayesian network
摘要
Abstract
In the field of multivariate alteration detection(MAD)of remote sensing images,change vector analysis in posterior probability space(CVAPS)is a widely used method.However,the CVAPS,which employs support vector machines to estimate the posterior probability vectors of remote sensing image pixels,is susceptible to various factors such as different objects with the same spectrum,the same object with different spectra,and mixed pixels in remote sensing images.These factors make it difficult to accurately estimate the magnitude and direction of the posterior probability vectors of complex pixels,consequently affecting the accuracy of multivariate alteration detection.Therefore,under the framework of CVAPS,this paper proposed a MAD method using angle thresholds,which employed the fuzzy C-means clustering to decompose mixed pixels and coupled a context-sensitive Bayesian network.When the angle is less than a certain threshold,the pixel is identified as the change type represented by the standard change vector.Experimental results show that the proposed algorithm exhibited superior alteration detection performance,achieving higher change detection accuracy than other algorithms.关键词
角度阈值/多元变化检测/模糊C均值/上下文敏感的贝叶斯网络/后验概率空间/变化向量分析Key words
angle threshold/multivariate alteration detection(MAD)/fuzzy C-means(FCM)/context-sensitive Bayesian network/posterior probability space/change vector analysis(CVA)分类
计算机与自动化引用本文复制引用
朱睿,李轶鲲,李小军,杨树文,谢江陵..基于上下文敏感贝叶斯网络的角度阈值多元变化检测[J].自然资源遥感,2025,37(5):131-140,10.基金项目
国家重点研发计划项目"边海重点区域安全态势异常感知与互联互通分析技术"(编号:2022YFB3903604)和国家自然科学基金项目"西北重点城市彩钢板建筑群与产业园区时空关联关系"(编号:42161069)共同资助. （编号:2022YFB3903604）
