Partially supervised contextual classification of multitemporal remotely sensed images

A key-problem in dealing with multitemporal images of a given geographical area is the identification of the changes occurring between distinct acquisition dates. A complete map of the change typologies can be generated when training data are available for all observation dates, but this completely supervised context involves expensive requirements. On the other hand, a completely unsupervised context does not require any prior information but does not allow an analysis of the different typologies of change, since no class information is available at any observation date. In the present paper, a contextual multitemporal classification and change detection algorithm is proposed, which deals with remotely sensed image sequences with ground truth information available only at none reference acquisition date. The method integrates clustering information with a two-stage contextual Markov Random Field (MRF) model for the spatio-temporal correlation associated to the sequence. The algorithm is validated on a multitemporal and multispectral real data set, acquired over an agricultural and urban area, and characterized by a large amount of changes between the observation dates.