HyperMODE: A Continuous-Depth Spectral–Spatial Modeling Framework with Mamba and Neural Ordinary Differential Equations for Hyperspectral Image Classification

Jialin Tang, Yunduan Lou, Yanhui Guo, Yu Bai

Abstract: Hyperspectral image (HSI) classification requires jointly capturing fine-grained spectral–spatial patterns and long-range contextual dependencies. However, existing convolutional, Transformer-based, and recent state-space architectures mainly rely on fixed discrete layer-wise transformations, which may lead to fragmented intermediate feature updates and insufficient coordination between multi-scale local representation learning and global spatial propagation. To address this issue, we propose HyperMODE, a unified continuous–discrete spectral–spatial modeling framework for HSI classification. Specifically, HyperMODE first constructs multi-scale spectral–spatial representations from compact input patches, and then couples Neural ODE-based continuous-depth feature evolution with scale-diverse Mamba-based spatial propagation through an interval-coupled pipeline. In this framework, Neural ODE enables smoother and more consistent feature transformation in the reduced representation space, while the multi-branch Mamba architecture performs efficient long-range spatial propagation and cross-scale context interaction with linear complexity. By unifying continuous feature evolution and efficient spatial propagation, HyperMODE establishes a coherent hybrid paradigm for spectral–spatial representation learning. Extensive experiments on four public HSI benchmarks, including Pavia University, WHU-Hi-HanChuan, WHU-Hi-LongKou, and Houston 2013, demonstrate stable and competitive classification performance, validating the effectiveness of the proposed framework.