Targeted Change Detection: a Novel Sensor-independent Partially-supervised Approach

In several real-world applications (e.g., forestry, agriculture), the objective of change detection is actually limited to one (or few) specific “targeted” land-cover transition(s) affecting a certain area in a given time period. In such cases, ground-truth information is generally available for the only land-cover classes of interest at the two dates, which limits (or hinders) the possibility of successfully employing standard supervised approaches. Moreover, even unsupervised change-detection methods cannot be effectively used, as they allow identifying all the areas experiencing any type of change, but not discriminating where specific land-cover transitions of interest occur. In this paper, we present a novel technique capable of addressing this challenging issue (formulated in terms of a compound decision problem) by exploiting the only ground truth available for the targeted land-cover classes at the two dates. In particular, the proposed method relies on a partially-supervised approach and jointly exploits the Expectation-Maximization (EM) algorithm and an iterative labelling strategy based on Markov random fields (MRF) accounting for spatial and temporal correlation between the two images. Moreover, it also allows handling images acquired by different sensors at the two investigated times. Experimental results on different multi-temporal and multi-sensor data sets confirmed the effectiveness and the reliability of the proposed technique, which provided change-detection accuracies comparable with those obtained by fully-supervised methods.