Deep Transfer Learning in Remote Sensing

本项目通过减少语义差异（领域转移）来解决遥感（RS）中从注释丰富的源域到注释稀缺的目标域的知识转移问题，从而帮助后者及其后续应用在不需要大量手动标记数据的情况下实现。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deep RS-TL框架。在此框架下，一方面，我们将开发几种核心深度迁移算法来解决遥感知识迁移的几个基本挑战，包括源-目标对准、多时间适应、多源适应、多尺度适应、空间-光谱适应、源与目标域之间的跨任务迁移、跨模态迁移。另一方面，我们将构建一个智能遥感图像标注软件，将所有已开发的算法集成在一起，实现灵活、个性化、智能化的标注，实现更高效的遥感标签采集。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，减轻其对费力标记数据的严重依赖。

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.