A data fusion approach to unsupervised change detection

This paper addresses the problem of change detection in multitemporal remote-sensing images. In particular, an approach to automatic unsupervised change detection, suitable to be used with multisource and multisensor data, is presented. This approach can be applied by exploiting two different data-fusion strategies: a pixel-based and a context-based strategy. The resulting robust change-detection method can also be applied to multispectral images by modeling each spectral channel as a separate information source, thus obtaining an alternative method to the standard change vector analysis technique. Experimental results confirm the effectiveness of the proposed approach.