Attention-based Domain Adaptation for Hyperspectral Image Classification

Machine learning algorithms have been extensively used to generate complex features for classification task in the hyperspectral images. However, for challenging cases like domain adaptation (DA), these algorithms tend to perform less efficiently. Recently, with the advent of deep learning algorithms, more complex but useful features can be generated for hyperspectral image classification task. However, attention-based feature generation is not explored till now, which has been found to be effective for distinguishing different classes of images than without transferring the parameters. In this paper, we have opted to use attention-based DA based on transferring different levels of attention from a supervisor network to the student network to provide useful but more complex features for improving the overall classification of the DA problem. It has been shown that the proposed attention-based transfer method outperforms the state-of-the-art domain adaptation methods.