Visual exploration of uncertainty in remote-sensing classification

Abstract

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