TinyCapsViT: Ultra-Lightweight Hyperspectral and Multispectral Image Classification for UAV Edge Deployment Using Capsule Vision Transformers

Hyperspectral image (HSI) classification is central to environmental monitoring, yet real-time deployment of deep learning models on resource-constrained edge platforms remains challenging due to high spectral dimensionality and computational overhead. In this paper, we propose TinyCapsViT, an ultra-lightweight hybrid architecture that integrates convolutional feature extraction, transformer-based attention, and capsule-inspired representation learning for efficient HSI classification under strict TinyML constraints. The model employs a minimal convolutional stem using pointwise and depthwise separable convolutions to capture local spectral-spatial features, followed by a compact tokenization strategy and learnable positional embeddings. A lightweight self-attention module enables global context modeling with reduced computational complexity, while a capsule-inspired refinement block with squash nonlinearity and residual scaling enhances feature discrimination. The proposed architecture contains only 2781 trainable parameters, representing up to a 30× reduction compared with larger architectures such as ResNet and Vision Transformer, and approximately 11× and 9.5× fewer parameters than the CNN and 3D-CNN baselines, respectively. Extensive experiments across benchmark hyperspectral datasets demonstrate that TinyCapsViT achieves up to 99.61% overall accuracy and maintains competitive classification performance despite its substantially reduced model complexity. Although TinyCapsViT does not consistently achieve the highest classification accuracy compared with larger baseline models, it provides a favourable trade-off between classification performance and computational efficiency. These results demonstrate the potential of TinyCapsViT as a practical solution for real-time hyperspectral analysis on resource-constrained edge platforms and UAV-based environmental monitoring applications.