Deep Transfer Learning in Remote Sensing

遥感深度迁移学习

• 批准号: 519016653
• 负责人: Professorin Dr.-Ing. Xiaoxiang Zhu
• 金额: --
• 依托单位: Lehrstuhl für Data Science in Earth Observation
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/519016653?language=en
• 关键词: Deep; Transfer; Learning; Remote; Sensing

This project addresses knowledge transfer in Remote Sensing (RS) from an annotation-rich source domain to an annotation-scarce target domain by reducing their semantic discrepancy (domain shift), helping the latter and its follow-up applications without the need of numerous manually-labeled data. Specifically, the goal of the project is to design a universal deep Transfer Learning (TL) framework for RS data, named as deep RS-TL framework. Within this framework, on the one hand, we will develop several core deep TL algorithms to tackle several fundamental challenges of transferring knowledge in remote sensing, including source-target alignment, multi-temporal adaptation, multi-source adaptation, multi-scale adaptation, spatial-spectral adaptation, cross-task TL, cross-modality TL between source and target domain. On the other hand, we will construct an intelligent RS imagery annotation software which integrates all developed algorithms, to achieve flexible, personal and intelligent annotation for more efficient RS label collection. As a result, the anticipated deep RS-TL framework will considerably facilitate the practical applications of machine learning in remote sensing by relaxing its heavy dependence on laboriously labeled data.

该项目解决了遥感（RS）中的知识转移，从一个注释丰富的源域到一个注释稀缺的目标域，通过减少它们的语义差异（域转移），帮助后者及其后续应用，而不需要大量的手动标记的数据。具体来说，该项目的目标是为RS数据设计一个通用的深度迁移学习（TL）框架，称为深度RS-TL框架。 在此框架下，一方面，我们将开发几个核心的深度TL算法来解决遥感知识转移的几个基本挑战，包括源目标对齐，多时相适应，多源适应，多尺度适应，空间光谱适应，跨任务TL，源和目标域之间的跨模态TL。另一方面，我们将构建一个智能化的遥感影像标注软件，集成了所有开发的算法，实现灵活，个性化和智能化的注释，更有效的遥感标签收集。因此，预期的深度RS-TL框架将大大促进机器学习在遥感中的实际应用，放松其对费力标记数据的严重依赖。