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.
翻译:对数拉迪奥(LR)操作器已被广泛用于生成合成孔径雷达图像变化检测的不同图像。 但是,这个像素操作器产生的不同图像可能会受到合成孔径雷达图像的分辨和咬时合成孔径雷达图像之间不可避免的登记错误的影响。 在本信中,我们提出了一个空间计量学习方法,通过从一组制约对中学习一套测量标准,获取对光谱更强的差异图像。 在拟议方法中,空间环境被考虑用于构建制约配对,每对配对由同一地点的咬牙合成孔雷达图像的补丁组成。 然后,通过使用最大边距标准优化可获得半不完全正数的正数矩阵 $\bf M$。 最后,我们核实了我们提议的关于四套具有挑战性的咬角合成孔径雷达图像数据集的方法。 实验结果表明,我们拟议方法获得的差异图比其他州一级方法差。