Automatic mass function estimation based Fuzzy-C-Means algorithm for remote sensing images change detection

In this paper, we present a new method for automatic mass function estimation and focal elements selection as a fundamental step to apply the Dempster-Shafer Theory (DST). The idea is to use the centroids and membership distributions obtained by applying the Fuzzy-C-Means algorithm (FCM) to define the mass function. The proposed method allows finding composite focal elements that represent the highest uncertainty and ambiguity. Experiments were conduced on multi-spectral and multi-temporal images for the purpose of change detection by integrating the proposed method of mass function estimation in a process of post-classification by DST. The proposed system of change detection is characterised by a multi-level of imperfection handling where the ambiguity is modelled firstly by FCM, then the uncertainty and the imprecision are handled in the step of mass function estimation. The effectiveness of the proposed methodology is demonstrated by applying it to find transformed region within two landsat images where we obtained high rates of classifications.