Determining uncertainties and their propagation in classified remotely sensed image-based dynamic change detection

This paper provides an approach to determine uncertainties and their propagation in remotely sensed images-based dynamic change detection. In this approach, uncertainties of a classified image using maximum likelihood classification method for each date is firstly determined. The probability vectors which are generated during maximum likelihood classification are used as the uncertainty indicators. The second problem is to determine uncertainty propagation when multi-images are compared to detect changes of land cover. The problem is defined by formulating them in a mathematical language to facilitate the following analyses. Two techniques are used to determine the propagation of uncertainties in the comparison two classified images. One is based on the product rule in probability theory and the other is based on the certainty factor (CF) model with probabilistic interpretation. The third problem is to represent uncertainties to communicate them to the users. Two forms of results are presented in the paper: (a) statistics tables and (b) 3D plus colour figures.