HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model

• URL: http://arxiv.org/abs/2406.11519v1
• Date: Mon, 17 Jun 2024 13:22:58 GMT
• Title: HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model
• Authors: Di Wang, Meiqi Hu, Yao Jin, Yuchun Miao, Jiaqi Yang, Yichu Xu, Xiaolei Qin, Jiaqi Ma, Lingyu Sun, Chenxing Li, Chuan Fu, Hongruixuan Chen, Chengxi Han, Naoto Yokoya, Jing Zhang, Minqiang Xu, Lin Liu, Lefei Zhang, Chen Wu, Bo Du, Dacheng Tao, Liangpei Zhang,
• Abstract summary: Hyper SIGMA is a vision transformer-based foundation model for HSI interpretation. It integrates spatial and spectral features using a specially designed spectral enhancement module. It shows significant advantages in scalability, robustness, cross-modal transferring capability, and real-world applicability.
• Score: 88.13261547704444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models (FMs) are revolutionizing the analysis and understanding of remote sensing (RS) scenes, including aerial RGB, multispectral, and SAR images. However, hyperspectral images (HSIs), which are rich in spectral information, have not seen much application of FMs, with existing methods often restricted to specific tasks and lacking generality. To fill this gap, we introduce HyperSIGMA, a vision transformer-based foundation model for HSI interpretation, scalable to over a billion parameters. To tackle the spectral and spatial redundancy challenges 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 450K 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, and real-world applicability.

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