文章基本信息

标题：Likelihood-based classification of high resolution images to generate the initial topology and geometry of land cover segments
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作者：Ali A. Abkar ; Nanno J. Mulder
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：1998
卷号：XXXII Part 4
出版社：Copernicus Publications
摘要：This paper’s origin is to reach for a way for automatic initiation of shape hypothesis for Model Based Image Analysis(MBIA) in the specific case of agricultural fields. A solution is to start with local (topological) hypotheses. The topologicaldata are integrated from local topology to the level of real 2-dimensional objects. The method requires radiometric modelto generate the normalized class membership probabilities (likelihood vectors) and a minimum-size-of-object parameter.The paper gives a detailed description of the analysis approach for initiating the shape hypothesis for MBIA andbesides resolves problems related to classical approaches such as per pixel maximum likelihood classification. Anexperiment is presented about its application in a case of RGB-CCD image of agricultural fields’ model. We obtained anoverall accuracy of 97% in comparison with 83% in improved maximum likelihood classification
关键词：Likelihood-based classification; Maximum likelihood classification; Geo-information systems; Remote;sensing; Agriculture; Shape hypothesis; Model-based image analysis