Adaptive Domain-Adversarial Few-Shot Learning for Cross-Domain Hyperspectral Image Classification

Abstract

The process of annotating hyperspectral image (HSI) data is characterized by its time-consuming and labor-intensive nature. To address this challenge, researchers often employ a meta-learning paradigm known as few-shot learning (FSL), which leverages source domains containing a substantial number of labeled samples to assist in the classification of target domains with limited labeled samples. Many existing FSL methods rely on a conditional domain-adversarial strategy to mitigate the domain shift between source and target domains. However, these methods overlook the fact that the degrees of conditional distribution discrepancies between the two domains can vary significantly across different classes, leading to suboptimal conditional distribution alignment. To address this problem, we propose a framework called Adaptive Domain-Adversarial Few-Shot Learning (ADAFSL). Overall, the proposed ADAFSL employs an adaptive strategy that assigns varying weights to the conditional adversarial losses for different classes based on their respective degrees of discrepancies, thereby achieving global conditional distribution alignment. Specifically, a local alignment score map is constructed by measuring the similarity between labeled and unlabeled samples using both Euclidean and class-covariance metrics. This map is then multiplied with the conditional adversarial loss map, thus allocating more emphasis to the classes exhibiting greater discrepancies between the two domains. Moreover, to enhance cross-domain FSL, we design a multi-scale spectral-spatial feature extraction (MSFE) module, which incorporates cascaded multi-scale dilated convolutions. Experimental results on four public HSI datasets demonstrate that the proposed ADAFSL outperforms other state-of-the-art methods. The source code of this method can be found at https://github.com/JieW-ww/ADAFSL.