SAR Image Change Detection via Spatial Metric Learning with an Improved Mahalanobis Distance
This work addresses change detection in SAR images, which is important for remote sensing applications, but it appears incremental as it builds on existing metric learning techniques.
The authors tackled the problem of speckle noise and registration errors in SAR image change detection by proposing a spatial metric learning method that uses an improved Mahalanobis distance to generate more robust difference images. Experimental results on four datasets showed that their method outperformed other state-of-the-art approaches.
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 pixel-wise 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 more robust to the speckle by learning a metric from a set of constraint pairs. In the proposed method, spatial context is considered in constructing constraint pairs, each of which consists of patches in the same location of bitemporal SAR images. Then, a semi-definite positive metric matrix $\bf M$ can be obtained by the optimization with the max-margin criterion. Finally, we verify our proposed method on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that the difference map obtained by our proposed method outperforms than other state-of-art methods.