文章基本信息

标题：Development of an advanced uncertainty measure for classified remotely sensed scenes
本地全文：下载
作者：Jochen Schiewe ; Christoph Kinkeldey
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：2009
卷号：XXXVIII-2/W11
出版社：Copernicus Publications
摘要：Classified remotely sensed data serves as the basis for various types of city models . Since the requirements concerning the correctness of these models are rapidly growing, the demands for a significant assurance of their quality increase as well . Standard methods for the a posteriori evaluation of classified data have successfully been applied but they do not fully meet the requirements resulting from recent developments, primarily the higher geometric and thematic accuracies of modern sensor systems. One consequence is that the uncertainties inherent in all kinds of data cannot be ignored anymore – not even those in the so-called ground truth data which is used as reference in the quality assessment process. Hence, we propose an integrated approach that considers uncertainties in both the classification and the reference data. The phenomenon of indeterminate boundaries – another effect of more accurate remote sensing data – is treated using a border model based on fuzzy logic. This paper describes the overall concept (section 1) as well as its key steps, the generation of transition zones including the fuzzification process (section 2) and the derivation of the advanced uncertainty measure (section 3). In section 4 we present an example application of the concept dealing with the evaluation of a classified orthophoto scene
关键词：classification; uncertainty; quality assessment; fuzzy borders; transition zones