Deep learning (DL) algorithms have become increasingly popular in recent years for remote sensing applications, particularly in the field of change detection. DL has proven to be successful in automatically identifying changes in satellite images with varying resolutions. The integration of DL with remote sensing has not only facilitated the identification of global and regional changes but has also been a valuable resource for the scientific community. Researchers have developed numerous approaches for change detection, and the proposed work provides a summary of the most recent ones. Additionally, it introduces the common DL techniques used for detecting changes in satellite photos. The meta-analysis conducted in this article serves two purposes. Firstly, it tracks the evolution of change detection in DL investigations, highlighting the advancements made in this field. Secondly, it utilizes powerful DL-based change detection algorithms to determine the best strategy for monitoring changes at different resolutions. Furthermore, the proposed work thoroughly analyzes the performance of several DL approaches used for change detection. It discusses the strengths and limitations of these approaches, providing insights into their effectiveness and areas for improvement. The article also discusses future directions for DL-based change detection, emphasizing the need for further research and development in this area.

中文翻译：

---

遥感变化检测深度学习的进展：综述

近年来，深度学习（DL）算法在遥感应用中变得越来越流行，特别是在变化检测领域。事实证明，深度学习能够成功地自动识别不同分辨率的卫星图像的变化。深度学习与遥感的结合不仅促进了全球和区域变化的识别，而且也成为科学界的宝贵资源。研究人员已经开发了多种变化检测方法，拟议的工作提供了最新方法的总结。此外，它还介绍了用于检测卫星照片变化的常见深度学习技术。本文中进行的荟萃分析有两个目的。首先，它跟踪了深度学习研究中变化检测的演变，突出了该领域取得的进步。其次，它利用强大的基于深度学习的变化检测算法来确定在不同分辨率下监控变化的最佳策略。此外，所提出的工作彻底分析了用于变化检测的几种深度学习方法的性能。它讨论了这些方法的优点和局限性，提供了对其有效性和需要改进的领域的见解。文章还讨论了基于深度学习的变化检测的未来方向，强调了该领域进一步研究和开发的必要性。