arXiv 1906.079302019-06-19cs.CVcs.LG

基于改进马氏距离的空间度量学习的SAR图像变化检测

SAR Image Change Detection via Spatial Metric Learning with an Improved Mahalanobis Distance

Rongfang Wang, Jia-Wei Chen, Yule Wang, Licheng Jiao, Mi Wang

DOI：: 10.1109/LGRS.2019.2915251

摘要：

对数比（LR）算子被广泛用于合成孔径雷达（SAR）图像变化检测中的差分图像生成。然而，这种逐像素算子产生的差分图像可能会受到SAR图像斑点和不可避免的配准误差之间的双时SAR图像。在这封信中，我们提出了一种空间度量学习方法，通过学习一组约束对的度量来获得对斑点更鲁棒的差分图像。在该方法中，空间上下文被认为是在构造约束对，其中每个由补丁在同一位置的双时SAR图像。然后，利用最大间隔准则进行优化，得到一个半正定度量矩阵$\bf M$.最后，我们验证了我们提出的方法对四个具有挑战性的双时SAR图像数据集。实验结果表明，我们提出的方法获得的差异图优于其他国家的最先进的方法。

英文摘要：

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.

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