深度迁移学习支持下的高分辨率遥感影像变化检测研究

地表变化的动态检测分析对于理解人类发展与自然现象之间的关系至关重要。目前，遥感技术可以实现高空间分辨率、大范围、高频次的持续观测，为探测、识别和分析变化提供了坚实的数据基础。方法上，监督变化检测可以详尽的展示变化类型的转移并因此得到了广泛应用，但往往依赖于充足且可靠的变化样本。然而，大量可靠样本的获取成本通常较高，严重制约了变化检测的精度及自动化水平。因此，本项目拟借助迁移学习对知识迁移的能力及深度学习高层次特征的自学习能力，提升高分辨率遥感变化检测的精度与智能化水平。研究内容包括：构建有标注历史场景向无标注变化场景的迁移模型，有效解决变化样本少的问题；通过对多时相高分辨率像对特征的相似测度，进行变化样本筛选以提高可靠性；建立多源知识联合的多任务变化检测框架，挖掘多时相遥感影像的相关特征，提升变化检测的精度。研究成果将为生态系统监测、城市规划、灾害评估等诸多方面的应用中提供理论和技术支撑。

The dynamic analysis of earth surface changes is crucial to understanding the relationship between human development and natural phenomena. At present, remote sensing technology can achieve continuous observation with high spatial resolution, wide range and high frequency, which provides a solid data base for detecting, identifying and analyzing changes. In fact, supervised change detection is widely used as it can show the shift of change types in detail, which often relies on adequate and reliable change samples. However, the cost of selecting a large number of reliable samples is usually high, which seriously restricts the accuracy and the level of automation of change detection. Therefore, this project aims to improve the accuracy and intelligence level of high-resolution remote sensing change detection by introducing the theories and methods of transfer learning and deep learning. It concentrates on three aspects: First of all, the project will develop a deep transfer learning network which can effectively solve the problem of scene transfer and automated sample selection. Secondly, the determination mechanism of reliable samples is then proposed to improve the reliability of the change detection samples by the similarity measure of the features of the multi-phase high resolution imagery. Finally, a multi-task framework using multi source knowledge fusion is established, which can mine the related deep features of multi temporal remote sensing images and improving the accuracy of change detection. The research results will provide theoretical and technical support for the applications of ecosystem monitoring, urban planning, disaster assessment and many other aspects.