Visual exploration of uncertainty in remote-sensing classification

Exploratory analysis of remotely-sensed data aims at acquiring insight as to the stability of possible classi cations of these data and their information value for speci c applications. For this purpose, knowledge of the uncertainties underlying these classi cations is imperative. In this paper, we introduce various measures that summarise for a classi cation, in a single number per pixel, the distribution and extent of the uncertainties involved. Since exploratory analysis needs e ective ways of conveying information to the user, we in addition address various ways of cartographic visualisation of uncertainty.