Hilbert-Schmidt Independence Learning for Cross-Domain Zero-Shot Hyperspectral Image Classification.

Abstract

Hyperspectral image classification (HSIC) has witnessed remarkable progress with the rise of deep learning. Nevertheless, its real-world deployment remains substantially constrained by two fundamental challenges: substantial domain discrepancies across heterogeneous sensors and the scarcity of labeled samples in the real scene (target domain). These issues motivate the task of cross-domain zero-shot HSIC (CDZS HSIC), where the objective is to recognize unseen target categories without any labeled supervision. To address this challenge, we develop a Hilbert-Schmidt independence learning guided Kolmogorov-Arnold Network (HSIL-KAN). Specifically, we first construct a Cross-Domain Kolmogorov-Arnold Network (CD-KAN) as the core backbone, enabling expressive spectral-spatial representation learning with compact parameterization. Building upon this backbone, we design a Hilbert-Schmidt Independence Criterion guided Domain Feature Decomposition (H-DFD) module to explicitly factorize latent features into domain-invariant and domain-specific subspaces, thereby substantially enhancing cross-domain transferability. Furthermore, a Prototype-Regularized Equiangular Tight Frame (PR-ETF) classifier is proposed to maintain geometric class separability and alleviate class imbalance in the zero-shot condition. Comprehensive experiments on three benchmark hyperspectral datasets demonstrate that HSIL-KAN consistently achieves state-of-the-art performance while also exhibiting notable parameter efficiency and computational scalability. The code will be available online at https://github.com/jojolee6513.