首份系统综述Transformer在高光谱图像分类中的应用与挑战
Transformers Meet Hyperspectral Imaging: A Comprehensive Study of Models, Challenges and Open Problems
- 梳理从预处理到损失设计的全流程组件选择
- 指出数据少、维度高、计算量大等核心难题
- 建议构建轻量化模型与可解释注意力机制
Transformer已成为捕捉长距离依赖的主流架构，但在高光谱成像（HSI）领域仍处于发展初期。我们回顾了截至2025年发表的300余篇论文，首次系统性地呈现基于Transformer的HSI分类研究全景。研究将典型流程分为预处理、分块或像素标记化、位置编码、空谱特征提取、多头自注意力变体、跳跃连接及损失设计等阶段，并对比不同设计选择与HSI独特的空谱特性之间的匹配关系。本文分析了领域发展的主要障碍：标注数据稀缺、极端高维光谱、计算开销大以及模型可解释性差。最后提出研究方向：推动高质量公开数据集建设，发展轻量化边缘模型，增强对光照与传感器变化的鲁棒性，开发内在可解释的注意力机制。旨在为研究人员提供组件选型与优化的实用指引。
原文摘要 · Abstract (English)
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.
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