Deep Transfer Learning in Remote Sensing

该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领域的知识转移，帮助后者及其后续应用程序不需要大量人工标记的数据。具体地说，该项目的目标是为遥感数据设计一个通用的深度迁移学习框架，称为深度RS-TL框架。在这个框架内，一方面，我们将开发几个核心的深层TL算法来解决遥感知识传输中的几个基本挑战，包括源-目标对齐、多时相自适应、多源自适应、多尺度自适应、空间-光谱自适应、跨任务TL、源和目标域之间的跨通道TL。另一方面，我们将构建一个集成所有已开发算法的遥感影像智能注记软件，以实现灵活、个性化和智能化的注记，从而更高效地收集遥感标签。因此，预期的深度RS-TL框架将通过放松机器学习对费力标记的数据的严重依赖，极大地促进机器学习在遥感领域的实际应用。

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.