Abstract. A well-performed difference map is very important for the change detection of remote sensing images. However, due to the influence of the lighting conditions and the change of the sensor, the difference maps often have low contrast between changed and unchanged pixels, which makes it difficult for subsequent cluster analysis. A coupled distance metric learning (CDML) model is proposed to solve the problem. The model attempts to learn a pair of mapping matrices and transform bi-temporal image data into a common feature space in which the contrast between the changed and unchanged pixels will be further enhanced. First, a sample selection mechanism is proposed to select training samples with high accuracy. Then, these samples are used to learn a pair of mapping matrices by minimizing the sum of the distances between the unchanged samples and maximizing the sum of the distances between the changed samples according to the CDML. Finally, the original images are mapped to the same feature space respectively by the mapping matrices, and the difference is calculated by L2 norm. The final experimental results confirm the feasibility and effectiveness of the proposed model.

中文翻译：

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基于耦合距离度量学习的遥感影像变化检测

摘要。一个表现良好的差异图对于遥感图像的变化检测非常重要。然而，由于光照条件和传感器变化的影响，差异图往往在变化像素和未变化像素之间对比度较低，这给后续的聚类分析带来了困难。提出了耦合距离度量学习（CDML）模型来解决该问题。该模型尝试学习一对映射矩阵，并将双时态图像数据转换为公共特征空间，其中变化和未变化像素之间的对比度将得到进一步增强。首先，提出了一种样本选择机制来选择高精度的训练样本。然后，这些样本用于通过根据 CDML 最小化不变样本之间的距离总和并最大化变化样本之间的距离总和来学习一对映射矩阵。最后通过映射矩阵将原始图像分别映射到相同的特征空间，并通过L2范数计算其差值。最终的实验结果证实了所提出模型的可行性和有效性。