Learning Discrete Semantic Invariance for Cross-Scene Hyperspectral Image Domain Generalization

Cross-scene hyperspectral image (HSI) classification requires a model trained on one labeled scene to recognize shared classes in an unseen scene without using target data. Existing domain-generalization methods often synthesize source variations and enforce representation consistency across transformed views. However, a transformation that suppresses scene-specific variation for one material may also alter a narrow spectral cue that distinguishes another class. Uniformly enforcing consistency across these transformations can therefore remove class-relevant information. In this work, we propose a transformation-conditioned discrete self-supervision (TCDS) framework for cross-scene hyperspectral image domain generalization, which learns shared discrete semantics by weighting transformed views according to their semantic preservation. For each source sample, our TCDS constructs plausible virtual scene variations and estimates how much class-relevant evidence each transformation retains. Spectral and spatial-context views are then encoded with separate discrete codebooks whose semantic indices are aligned before forming a preservation-weighted consensus. A complementary residual branch retains class-discriminative information outside the shared semantic codes and contributes to prediction through a preservation-dependent gate. Extensive experiments on three datasets demonstrate the effectiveness and superiority of our proposed TCDS method. The code file is public at https://github.com/Sky-byte-box/TCDS_code.