Sequential cascade classification of image time series by exploiting multiple pairwise change detection

This paper presents a novel sequential cascade classification technique for automatically updating land-cover maps by classifying remote sensing image time series. We assume that a reliable training set is initially available only for one of the images (i.e., the source domain) in the time series, whereas it is not for an image being classified (i.e., the target domain). Unlike the standard cascade classification method, the proposed method aims at exploiting all the images in the time series acquired between the target and source domains to effectively classify the target domain. To this end, initially `pseudo´ training sets of the images are defined by a multiple pairwise change detection based transfer learning strategy. Then, the target domain is classified by the proposed sequential cascade classification method, exploiting the temporal correlation between images. Experimental results obtained on a time series of Landsat multispectral images show the effectiveness of the proposed technique with respect to the standard cascade classification.