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Change detection approach on multitemporal Radarsat-1 SAR imagery for port surveillance

This paper proposed a difference of standard deviation operator to detect temporal changes in multi-temporal Radarsat-1 SAR images which is derived from the classical multiplicative model. When changes are occurred, textures of the terrestrial surface will be modified resulting in the variation of standard deviation of the terrestrial surface. The new Riemannian metric is introduced into the unsupervised classification algorithm to classify the changed areas. Experiments were carried on the Radarsat-1 images collected on the port of Yokohama in Kanagawa, Japan. Quantitative results point out the proposed technique is effective in detecting changes.