Detection of land-cover transitions in multitemporal images with active-learning based compound classification

This paper presents a novel active learning (AL) technique for the compound classification of multitemporal remote-sensing images for the detection of land-cover transitions. The proposed AL technique is based on the selection of unlabeled pairs of samples that have maximum uncertainty on their labels assigned by a classifier implemented according to the Bayes rule for compound classification. Uncertainty of a pair of samples is assessed by joint entropy defined on the basis of two different simplifying assumptions: i) class-conditional independence, and ii) temporal independence between multitemporal images. Accordingly, two algorithms for the proposed joint entropy based AL technique are introduced. The proposed joint entropy based AL algorithms are compared to each other and with a marginal entropy (entropy computed separately on single-date images) based AL technique. Experimental results obtained on two multispectral images show the effectiveness of the proposed technique.