综述基础模型在遥感图像变化检测中的应用进展
Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey
- 梳理基础模型在遥感变化检测中的技术路径与融合方法
- 总结多源数据融合与跨时相特征对齐的关键进展
- 适合遥感、计算机视觉交叉领域研究者参考
变化检测是遥感领域重要且广泛应用的技术，旨在分析地表随时间的变化，广泛应用于环境监测、城市扩张和土地利用分析。近年来，深度学习尤其是基础模型的发展，为特征提取与数据融合提供了更强大的解决方案，有效应对了复杂场景下的挑战。本文系统回顾了变化检测领域的最新进展，重点聚焦基础模型在遥感任务中的应用，涵盖模型架构、训练策略及典型应用场景，为后续研究提供全面的参考框架。
原文摘要 · Abstract (English)
Change detection, as an important and widely applied technique in the field of remote sensing, aims to analyze changes in surface areas over time and has broad applications in areas such as environmental monitoring, urban development, and land use analysis.In recent years, deep learning, especially the development of foundation models, has provided more powerful solutions for feature extraction and data fusion, effectively addressing these complexities. This paper systematically reviews the latest advancements in the field of change detection, with a focus on the application of foundation models in remote sensing tasks.
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