对比18种U-Net变体在遥感变化检测中的表现，指导模型选型。
A Comparative Study of U-Net Architectures for Change Detection in Satellite Images
- 系统比较18种U-Net变体在遥感变化检测中的性能差异。
- 发现双时相数据处理与长距离依赖建模对精度提升关键。
- 适合遥感变化检测任务的模型选择提供实证参考。
遥感变化检测对于监测地球表面动态变化至关重要。U-Net架构因其捕捉空间信息和进行像素级分类的能力而广受关注，但在遥感领域的应用仍需深入探索。本文通过综合分析34篇相关论文，对18种不同的U-Net变体进行了比较研究，评估其在遥感变化检测中的潜力。研究不仅分析了各类变体的优缺点，还特别强调了专为变化检测设计的模型，如采用孪生结构的Swin-U-Net。结果表明，有效处理不同时相数据及建模长距离依赖关系，对提升检测精度具有重要意义。本研究为遥感领域研究人员和实践者选择合适的U-Net版本提供了重要参考。
原文摘要 · Abstract (English)
Remote sensing change detection is essential for monitoring the everchanging landscapes of the Earth. The U-Net architecture has gained popularity for its capability to capture spatial information and perform pixel-wise classification. However, their application in the Remote sensing field remains largely unexplored. Therefore, this paper fill the gap by conducting a comprehensive analysis of 34 papers. This study conducts a comparison and analysis of 18 different U-Net variations, assessing their potential for detecting changes in remote sensing. We evaluate both benefits along with drawbacks of each variation within the framework of this particular application. We emphasize variations that are explicitly built for change detection, such as Siamese Swin-U-Net, which utilizes a Siamese architecture. The analysis highlights the significance of aspects such as managing data from different time periods and collecting relationships over a long distance to enhance the precision of change detection. This study provides valuable insights for researchers and practitioners that choose U-Net versions for remote sensing change detection tasks.
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