Few-Shot Learning Using Residual Channel Attention and Prototype Domain Adaptation for Hyperspectral Image Classification

While deep learning has been widely employed for the classification of hyperspectral imagery, many scenarios arise in practice in which too few labeled samples exist to effectively train the networks. Few-shot learning has been recently used to deploy classifiers trained on source-domain datasets comprising a large number of labeled samples to datasets from a target domain with only few labeled samples. However, most techniques in this vein effectively assume that the source and target domains possess the same data distribution, whereas the distributions between the two domains often differ widely in practice. Adversarial domain adaption driven by prototype classifiers deployed independently in the source and target domains is proposed to handle such differing source and target distributions, while an attention-based feature extractor with residual skip connections is developed in order to weight spectral bands according to their importance to the hyperspectral classification task. Experimental results demonstrate improved performance for the proposed few-shot-learning framework relative to both fully-supervised classifiers as well as other few-shot techniques.