期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B7
页码：959-964
出版社：Copernicus Publications
摘要：A new object-level change detection(OLCD) approach, combining object analysis with change detection process is proposed for land surface monitoring. The object analysis is consisting of Mean Shift(MS)&Region Grow(RG) multiscale segmentation, Support Vector Machine(SVM) classification and object footprint. Change detection process is composed of object overlay analysis(OOA),class attributes comparison and accuracy assessement.Depending on this approach,we can detect the change type of objects according to classification label. Furthermore,object boundaries are extracted assisted on the vector tool, and the detection result in the form of vector data can be used to update GIS database in the land use/cover(LUCC) change. The OLCD approach performances were assessed using multisource SPOT-5 and IKNOS reference data in Jiaxing, and were compared to a pixel-based method using post classification comparison in CASMImgeInfo3.5.High overall accuracy(>85%) was achieved by object-level method.The experiment result illustrated the approach could make full use of contextual information of objects and effectively detect object changes
