An Adaptive Semisupervised Approach to the Detection of User-Defined Recurrent Changes in Image Time Series

In this paper, we present a novel domain adaptation technique aimed at providing reliable change detection maps for a series of image pairs acquired on the same area at different times. The proposed technique exploits the polar change vector analysis method and assumes that the reference data for characterizing a specific change of interest are available only for a pair of images (source domain). Then, it exploits the knowledge learned from the source domain and adapts it to other pairs of images belonging to the time series (target domains) to be analyzed. The proposed technique is able to handle possible radiometric differences among images adapting in an unsupervised way the decision rule estimated on the source domain to the target domains through variables estimated directly on the target images. The proposed approach has been applied to two data sets made up of time series of Landsat Thematic Mapper images. In one case, the change of interest is related to evolution of deforestation, while in the other case, it is related to burned area detection. Experimental results show the effectiveness of the proposed technique.