HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model
Wang, Di Hu, Meiqi Jin, Yao Miao, Yuchun Yang, Jiaqi Xu, Yichu Qin, Xiaolei Ma, Jiaqi Sun, Lingyu Li, Chenxing
Wang, Di
Hu, Meiqi
Jin, Yao
Miao, Yuchun
Yang, Jiaqi
Xu, Yichu
Qin, Xiaolei
Ma, Jiaqi
Sun, Lingyu
Li, Chenxing
Supervisor
Department
Computer Vision
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Abstract
Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring. However, existing HSI processing methods are predominantly task-specific and scene-dependent, which severely limits their ability to transfer knowledge across tasks and scenes, thereby reducing the practicality in real-world applications. To address these challenges, we present HyperSIGMA, a vision transformer-based foundation model that unifies HSI interpretation across tasks and scenes, scalable to over one billion parameters. To overcome the spectral and spatial redundancy inherent in HSIs, we introduce a novel sparse sampling attention (SSA) mechanism, which effectively promotes the learning of diverse contextual features and serves as the basic block of HyperSIGMA. HyperSIGMA integrates spatial and spectral features using a specially designed spectral enhancement module. In addition, we construct a large-scale hyperspectral dataset, HyperGlobal-450K, for pre-training, which contains about 450 K hyperspectral images, significantly surpassing existing datasets in scale. Extensive experiments on various high-level and low-level HSI tasks demonstrate HyperSIGMA’s versatility and superior representational capability compared to current state-of-the-art methods. Moreover, HyperSIGMA shows significant advantages in scalability, robustness, cross-modal transferring capability, real-world applicability, and computational efficiency.
Citation
D. Wang et al., "HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 8, pp. 6427-6444, Aug. 2025, doi: 10.1109/TPAMI.2025.3557581
Source
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conference
Keywords
Remote sensing, hyperspectral image, foundation model, attention, vision transformer, large-scale dataset
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Source
Publisher
IEEE