Unsupervised change detection in multispectral images based on independent component analysis

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. The paper proposes a data dependent change detection approach based on textural features extracted by the Independent Component Analysis (ICA) model. The properties of ICA allow to create energy features for computing multispectral and multitemporal difference images to be classified. Our experiments on remote sensing images show that the proposed method can efficiently and effectively classify temporal discontinuities corresponding to changed areas over the observed scenes