SAR Image Change Detection via Spatial Metric Learning With an Improved Mahalanobis Distance
The log-ratio (LR) operator has been widely employed to generate the difference image for synthetic aperture radar (SAR) image change detection. However, the difference image generated by this pixelwise operator can be subject to SAR images speckle and unavoidable registration errors between bitemporal SAR images. In this letter, we proposed a spatial metric learning method to obtain a difference image that is more robust to the speckle by learning a metric from a set of constraint pairs. In the proposed method, the spatial context is considered in constructing constraint pairs, each of which consists of patches in the same location of bitemporal SAR images. Then, a semidefinite positive metric matrix M can be obtained by the optimization with the max-margin criterion. Finally, we verify our proposed method on four challenging data sets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-the-art methods.