In the conventional change detection (CD) pipeline, two manually registered and labeled remote sensing datasets serve as the input of the model for training and prediction. However, in realistic scenarios, data from different periods or sensors could fail to be aligned as a result of various coordinate systems. Geometric distortion caused by coordinate shifting remains a thorny issue for CD algorithms. In this paper, we propose a reusable self-supervised framework for bitemporal geometric distortion in CD tasks. The whole framework is composed of Pretext Representation Pre-training, Bitemporal Image Alignment, and Down-stream Decoder Fine-Tuning. With only single-stage pre-training, the key components of the framework can be reused for assistance in the bitemporal image alignment, while simultaneously enhancing the performance of the CD decoder. Experimental results in 2 large-scale realistic scenarios demonstrate that our proposed method can alleviate the bitemporal geometric distortion in CD tasks.

翻译：在传统的变化检测（CD）流程中，经过人工配准和标注的两期遥感数据集被用作模型训练和预测的输入。然而，在实际场景中，由于不同坐标系的存在，不同时期或不同传感器获取的数据可能无法对齐。坐标偏移引起的几何畸变仍然是变化检测算法面临的一个棘手问题。本文提出了一种可重用的自监督框架，用于处理变化检测任务中的双时相几何畸变。该框架由预文本表示预训练、双时相图像对齐和下流解码器微调三个部分组成。通过单阶段预训练，框架的关键组件可被重复用于辅助双时相图像对齐，同时提升变化检测解码器的性能。在两个大规模真实场景下的实验结果表明，所提方法能够缓解变化检测任务中的双时相几何畸变问题。