Hyperspectral Open-Set Classification via Frequency-Domain Rotation Enhancement and Multi-Branch Adversarial Routing

Hyperspectral images have become indispensable for advanced material characterization and environmental monitoring, yet conventional analytical frameworks struggle with the evolving nature of spectral signatures in open-world scenarios. Open-set classification addresses this fundamental limitation by enabling recognition of both known and novel spectral categories during inference. Key technical barriers include rotational invariance in multi-angle acquisitions, multi-scale feature compatibility across spectral resolutions, frequency-domain discriminative decay, and interference from morphologically similar compounds. To overcome these challenges, we propose a Frequency-Domain Multi-Branch Adversarial Routing Open-Set Network (FDMAR-OSNet) integrating four core innovations: Fractional Fourier Transform layers for rotation-equivariant spectral localization, Multi-Branch Dynamic Gate Routing (MBDGR) for uncertainty quantified hierarchical feature fusion, Dual-Frequency Enhancement (DFE) modules separating diagnostic spectral components through learned frequency gates, and a Multi-Scale Adaptive Dynamic Adversarial Spectral (MS-ADAS) mechanism enabling joint spectral-spatial attention refinement. The mathematical co-design of adaptive spectral operators and uncertainty-aware architectures establishes new theoretical foundations for robust open-set analysis in dynamic spectral environments.