标题： 基于双向域适应的跨域高光谱图像分类 显示英文标题

标题： Cross-domain Hyperspectral Image Classification based on Bi-directional Domain Adaptation

摘要： 利用高光谱遥感技术可以提取细粒度的土地覆盖类别。 通常，用于训练和测试的卫星或航空图像来自不同的区域或时间，其中同一类在不同场景中具有显著的光谱偏移。 本文提出了一种双向域适应（BiDA）框架，用于跨域高光谱图像（HSI）分类，该框架专注于在独立适应空间中提取域不变特征和域特定信息，从而增强对目标场景的适应性和可分性。 在提出的BiDA中，设计了一个带有语义分词器的三分支Transformer架构（源分支、目标分支和耦合分支）作为主干网络。 具体来说，源分支和目标分支独立学习源域和目标域的适应空间，在耦合分支中开发了耦合多头交叉注意力（CMCA）机制，用于特征交互和域间相关性挖掘。 此外，设计了一个双向蒸馏损失，使用域间相关性指导适应空间的学习。 最后，我们提出了一种自适应强化策略（ARS），以在噪声条件下鼓励模型在源场景和目标场景中关注特定的通用特征提取。 在跨时相/场景的航空和卫星数据集上的实验结果表明，所提出的BiDA显著优于一些最先进的域适应方法。 在跨时相树种分类任务中，所提出的BiDA比最先进方法高出超过3%$\sim$5%。 代码将从以下网站提供：https://github.com/YuxiangZhang-BIT/IEEE_TCSVT_BiDA. 显示英文摘要

摘要： Utilizing hyperspectral remote sensing technology enables the extraction of fine-grained land cover classes. Typically, satellite or airborne images used for training and testing are acquired from different regions or times, where the same class has significant spectral shifts in different scenes. In this paper, we propose a Bi-directional Domain Adaptation (BiDA) framework for cross-domain hyperspectral image (HSI) classification, which focuses on extracting both domain-invariant features and domain-specific information in the independent adaptive space, thereby enhancing the adaptability and separability to the target scene. In the proposed BiDA, a triple-branch transformer architecture (the source branch, target branch, and coupled branch) with semantic tokenizer is designed as the backbone. Specifically, the source branch and target branch independently learn the adaptive space of source and target domains, a Coupled Multi-head Cross-attention (CMCA) mechanism is developed in coupled branch for feature interaction and inter-domain correlation mining. Furthermore, a bi-directional distillation loss is designed to guide adaptive space learning using inter-domain correlation. Finally, we propose an Adaptive Reinforcement Strategy (ARS) to encourage the model to focus on specific generalized feature extraction within both source and target scenes in noise condition. Experimental results on cross-temporal/scene airborne and satellite datasets demonstrate that the proposed BiDA performs significantly better than some state-of-the-art domain adaptation approaches. In the cross-temporal tree species classification task, the proposed BiDA is more than 3\%$\sim$5\% higher than the most advanced method. The codes will be available from the website: https://github.com/YuxiangZhang-BIT/IEEE_TCSVT_BiDA.