Dual-Branch Domain Adaptation Few-Shot Learning for Hyperspectral Image Classification

Abstract

Cross-domain few-shot learning (FSL) often employs adversarial domain adaptation techniques to address the issue of data distribution discrepancies between the source and target domains. However, forcing the alignment of two distinct domains may lead to distortions in class distribution alignment and result in a decrease in classification performance in hyperspectral image analysis. Moreover, existing cross-domain methods are often applied to satellite/airborne hyperspectral image as both the source and target domain. It is rarely explored whether the same cross-domain methods can be applied for cross applications where the source domain and target domain data could be both satellite/airborne hyperspectral image with lower spatial resolution and unmanned aerial vehicle (UAV) hyperspectral image with higher spatial resolution. To address these issues, this paper proposes a novel domain-adaptive FSL network with dual branches respectively aiming at domain fusion and domain separation. The domain fusion branch uses a conditional adversarial network to align the global distributions of the two domains, while the domain separation branch introduces gate mechanism for discriminative feature learning in each domain to achieve independent category distributions. During the experiment, the proposed method is evaluated by performing cross-transfer learning under the condition that low spatial resolution hyperspectral data and high spatial resolution hyperspectral data are used as source and target data alternately. The experimental results suggest that the proposed method not only mitigates the negative effects of forced alignment in domain fusion but also holds potential for cross-domain transfer learning between low and high spatial resolution hyperspectral images.