Attention-Based Domain Adaptation Using Residual Network for Hyperspectral Image Classification

Abstract

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.

Keywords

• Activation mapping
• attention mappings
• hyperspectral image (HSI)
• knowledge distillation (KD)
• residual network
• transfer learning