Hi-RWKV: Hierarchical RWKV Modeling for Hyperspectral Image Classification.

Abstract

Hyperspectral image (HSI) classification demands models that can jointly capture long-range spatial relations and high-dimensional spectral structures while remaining scalable to large scenes and robust under limited supervision. Existing CNN-, Transformer-, and state-space-based approaches either suffer from restricted receptive fields, quadratic attention complexity, or directional biases that hinder dense pixel-wise prediction. To address these limitations, we propose Hi-RWKV, a hierarchical recurrent weighted key-value framework tailored for hyperspectral analysis. Hi-RWKV introduces three key innovations: 1) a spatial structure-guided bidirectional propagation mechanism that integrates global spatial context while preserving boundary fidelity via edge-aware gating; 2) a spectral identity-driven channel mixing module that incorporates learnable band embeddings and whitening transforms to enhance cross-band discriminability; and 3) a multi-stage hierarchical encoder that progressively refines spectral-spatial representations with strictly linear complexity. Together, these designs enable efficient, direction-free spectral-spatial reasoning essential for large-scale HSI interpretation. Extensive experiments on four benchmarks demonstrate that Hi-RWKV consistently achieves state-of-the-art accuracy under diverse training regimes. Ablation studies confirm that each proposed module offers complementary gains in boundary preservation, spectral discrimination, and data efficiency. By unifying scalable recurrence with hyperspectral-specific structural modeling, Hi-RWKV establishes a strong and efficient paradigm for high-resolution remote sensing. The logs and source data of this article are available at https://github.com/HSI-Lab/Hi-RWKV.