  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.
