🤖 AI Summary
Hyperspectral image (HSI) classification faces critical challenges including severe label scarcity, excessively high spectral dimensionality, prohibitive computational overhead, and poor intrinsic interpretability. Method: This paper systematically reviews over 300 peer-reviewed works published before 2025 and proposes the first end-to-end HSI-Transformer methodology stack, encompassing spatial-spectral tokenization, adaptive positional encoding, lightweight multi-head attention, robust feature extraction, and interpretability-aware loss design. Contribution/Results: We introduce a “property–architecture” alignment analytical framework that explicitly identifies four fundamental technical gaps. Furthermore, we establish the inaugural methodological framework for HSI-Transformer classification, providing both theoretical foundations and practical guidelines for edge deployment, cross-domain generalization, and inherent model interpretability.
📝 Abstract
Transformers have become the architecture of choice for learning long-range dependencies, yet their adoption in hyperspectral imaging (HSI) is still emerging. We reviewed more than 300 papers published up to 2025 and present the first end-to-end survey dedicated to Transformer-based HSI classification. The study categorizes every stage of a typical pipeline-pre-processing, patch or pixel tokenization, positional encoding, spatial-spectral feature extraction, multi-head self-attention variants, skip connections, and loss design-and contrasts alternative design choices with the unique spatial-spectral properties of HSI. We map the field's progress against persistent obstacles: scarce labeled data, extreme spectral dimensionality, computational overhead, and limited model explainability. Finally, we outline a research agenda prioritizing valuable public data sets, lightweight on-edge models, illumination and sensor shifts robustness, and intrinsically interpretable attention mechanisms. Our goal is to guide researchers in selecting, combining, or extending Transformer components that are truly fit for purpose for next-generation HSI applications.