Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification

Hyperspectral (HS) images captured from Earth by satellite and aircraft have become increasingly important in several environmental and ecological contexts (e.g. agriculture and urban areas). In the present study we propose an iterative learning methodology for the change detection of HS scenes taken at different times in the same areas. It cascades clustering and classification through iterative learning, in order to separate salient regions, where a change occurs in the scene from the unchanged background. The iterative learning is evaluated in both the clustering and the classification steps. The experiments performed with the proposed methodology provide encouraging results, also compared to several recent state-of-the-art competitors.