ConvVitMamba: Efficient Multiscale Convolution, Transformer, and Mamba-Based Sequence modelling for Hyperspectral Image Classification

ABSTRACT Hyperspectral image (HSI) classification remains a challenging task due to the high spectral dimensionality of the data, strong spectral redundancy, and limited availability of labelled samples. Although convolutional neural networks (CNNs) and Vision Transformers (ViTs) have demonstrated strong performance by exploiting spectral-spatial information and long-range dependencies, they often suffer from high computational complexity and large parameter counts, which limit their practical applicability. To address these limitations, a unified hybrid framework, termed ConvVitMamba, is proposed for efficient hyperspectral image classification. The proposed architecture integrates three complementary components within a single model: a multiscale convolutional feature extractor for capturing local spectral, spatial, and spectral-spatial patterns; a Vision Transformer-based tokenization and encoding stage for modelling global contextual relationships; and a lightweight Mamba-inspired gated sequence mixing module for efficient content-aware sequence refinement without relying on quadratic-complexity self-attention. Principal Component Analysis (PCA) is employed as a preprocessing step to reduce spectral redundancy and improve computational efficiency. Extensive experiments are conducted on four benchmark hyperspectral datasets, including Houston and three UAV-borne QUH datasets (Pingan, Qingyun, and Tangdaowan). Quantitative results, evaluated using Overall Accuracy, Average Accuracy, and the Kappa coefficient, demonstrate that ConvVitMamba consistently outperforms state-of-the-art CNN-, Transformer-, and Mamba-based methods while maintaining a favourable balance between classification accuracy, model size, and inference efficiency. Ablation studies further confirm the complementary contributions of the multiscale convolutional, transformer, and Mamba-inspired components. These results indicate that the proposed framework provides an effective and efficient solution for hyperspectral image classification under both urban and natural scene settings. The source code is publicly available at https://github.com/mqalkhatib/ConvVitMamba