In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature. These approaches claim to offer state-of-the-art performance on different standard benchmark datasets. However, has the field truly made significant progress? In this paper we perform experiments which conclude a simple U-Net segmentation baseline without training tricks or complicated architectural changes is still a top performer for the task of change detection.

翻译：近年来，遥感领域文献中提出的变化检测深度学习架构呈爆炸式增长。这些方法声称在不同标准基准数据集上达到了最先进的性能。然而，该领域是否真的取得了实质性进展？本文通过实验得出结论：一个简单的U-Net分割基准模型，无需训练技巧或复杂的架构改动，仍然是变化检测任务中的顶尖表现者。