Discriminant Models for Uncertainty Characterization in Remotely Sensed Land Cover
Jingxiong Zhang*, Jiong You**, and Yunwei Tang
School of Remote Sensing and Information Engineering Wuhan University Wuhan 430079, China
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Abstract: Discriminant space defining area classes is an important conceptual model for uncertainty characterization in area-class maps. It needs to be adapted for use with real data sets, as area classes intended are rarely completely and unambiguously defined by empirical data classes. This paper explores its applications in land cover mapping and land cover change analyses. Through experiments using real data sets, it was found that there are significant differences between the results obtained by referring to data classes and those by information classes, and uncertainty characterization is well supported by discriminant models and geostatistics, which accommodate spatio-temporal interdependence in error occurrences and enable quantification of effects due to partially random measurement errors and systematic categorical discrepancy, respectively.
Keywords: uncertainty, area classes, discriminant space, geostatistics, land cover change