In remote sensing images, domain adaptation (DA) deals with the regions where labeling information is unknown. Typically, hand-driven features for learning a common distribution among known and unknown regions have been extensively exploited to perform the classification task in hyperspectral images with the aid of state-of-the-art machine learning algorithms. Under limited training samples and using hand-crafted features, the classification performance degrades significantly. To overcome the engineered feature extraction process, an automatic feature extraction scheme can be seen useful to generate more complex but useful features for classification. Deep-learning-based architectures have been found to be pivotal on this regard. Deep learning algorithms are effectively used in hyperspectral domain to solve the DA problem. However, attention-based activation mappings, which are very successful for distinguishing different classes of images via transferring relevant mappings from a deep-to-shallow network is not widely explored in DA domain. In this article, we have opted to use attention-based DA through transferring different levels of attentions by means of different types of activation mappings from a deep residual teacher network to a shallow residual student network. Our goal is to provide useful but more complex features to the shallow student network for improving the overall classification in case of DA task. It has been shown that for different kinds of activation mappings, the proposed attention-based transfer improves the performance of the shallow network for the DA problem. It also outperforms the state-of-the-art DA methods based on traditional machine learning and deep learning paradigms.

中文翻译：

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使用残差网络进行高光谱图像分类的基于注意力的域适应

在遥感图像中，域自适应 (DA) 处理标记信息未知的区域。通常，在最先进的机器学习算法的帮助下，用于学习已知和未知区域之间共同分布的手动特征已被广泛用于在高光谱图像中执行分类任务。在有限的训练样本和使用手工制作的特征下，分类性能显着下降。为了克服工程特征提取过程，可以看到自动特征提取方案对于生成更复杂但有用的分类特征很有用。已发现基于深度学习的架构在这方面至关重要。深度学习算法有效地用于高光谱领域以解决 DA 问题。然而，基于注意力的激活映射，通过从深到浅的网络传输相关映射来区分不同类别的图像非常成功，但在 DA 领域并未得到广泛探索。在本文中，我们选择使用基于注意力的 DA，通过不同类型的激活映射将不同级别的注意力从深度残差教师网络转移到浅残差学生网络。我们的目标是为浅层学生网络提供有用但更复杂的特征，以在 DA 任务的情况下改进整体分类。已经表明，对于不同类型的激活映射，所提出的基于注意力的转移提高了浅层网络在 DA 问题上的性能。