非局部均值加权的动态模糊Fisher分类器的遥感图像变化检测  

Change Detection in Multitemporal Remote Sensing Images Based on Dynamic Fuzzy Fisher Classifier and Non Local Mean Weighted Method

作　　者：辛芳芳[1,2] 焦李成[2] 王桂婷[2] 

机构地区：[1]西安微电子技术研究所,陕西西安710019 [2]西安电子科技大学智能感知与图像理解教育部重点实验室,陕西西安710071

出　　处：《测绘学报》2012年第4期584-590,共7页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(61003198);高等学校学科创新引智计划(111计划)(B07048);国家部委科技项目(9140A07011810DZ0107);教育部重点项目(108115)

摘　　要：提出一种新的变化检测算法,利用改进的动态模糊Fisher分类器,通过对多时相图像的联合直方图进行分类得到变化区域。在此基础上,根据图像空间关系对待检测点进行非局部均值加权,并以一定比例选取可靠性高的数据先进行标类,增加数据的可分性和算法的可靠性。根据更新后的样本动态调整待检测点权重及分类器参数,直到所有点判别完毕为止。本算法不受参数模型限制,不受差异算子影响并充分利用图像的空间与时间信息。真实遥感数据试验表明本算法提高了检测精度。A novel change detection approach based on the dynamic fuzzy Fisher classifier for multitemporal remote sensing images is proposed,which detects the changes with the joint histogram.To increase the separability of the unlabeled pixels,a non-local mean weighted method is used to introduce the spatial information.The unlabeled pixels are labeled with a predefined probability based on their predictive values.The weights of the unlabeled pixels and the parameters of the dynamic classifier are adjusted according to the updated samples until all the pixels are classified.The proposed method is distribution free,context-sensitive and not affected by the comparison operators.Experimental results on real multitemporal remote sensing images confirm the effectiveness of the proposed technique.

关 键 词：变化检测 动态模糊Fisher分类器 非局部均值加权 联合直方图 

分 类 号：P237.3[天文地球—摄影测量与遥感]