Detection of land-cover transitions in multitemporal images with a joint entropy based active-learning method

This paper presents a novel active learning method to detect land-cover transitions, which is defined in the framework of the Bayes rule for compound classification. Compound classification is a supervised technique that requires a suitable multitemporal training set for modeling the temporal correlation between multitemporal images. The temporal correlation is represented by the prior joint probabilities of classes which allow one to obtain accurate land-cover transitions maps. However, the collection of labeled samples is time consuming as well as costly. In this paper, a novel active learning method based on joint entropy is proposed to properly increase the number of initial multitemporal training samples by taking into account the temporal correlation between multitemporal images. Experimental results confirmed the effectiveness of the proposed joint entropy based active learning method for compound classification.