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：2012
卷号：XXXIX-B3
页码：467-472
DOI：10.5194/isprsarchives-XXXIX-B3-467-2012
出版社：Copernicus Publications
摘要：To deal with the problem of spectral variability in high resolution satellite images, this paper focuses on the analysis and modelling of spatial autocorrelation feature. The semivariograms are used to model spatial variability of typical object classes while Getis statistic is used for the analysis of local spatial autocorrelation within the neighbourhood window determined by the range information of the semivariograms. Two segmentation experiments are conducted via the Fuzzy C -Means (FCM) algorithm which incorporates both spatial autocorrelation features and spectral features, and the experimental results show that spatial autocorrelation features can effectively improve the segmentation quality of high resolution satellite images
