The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
SpectralGPT: Spectral Remote Sensing Foundation Model
Open accessAbstract
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS Big Data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; and 4) trains on one million spectral RS images, yielding models with over 600 million parameters. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS Big Data applications within the field of geoscience across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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Learning Transferable Visual Models From Natural Language Supervision
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“SpectralGPT is constructed following the MAE architecture [25] and incorporates spectral-wise tensor (3D) masking, where 90% of the tokens are masked out.”
BackgroundMethodInfluentialSelf-citationDeep learning and process understanding for data-driven Earth system science
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat (2019) · Nature · doi:10.1038/s41586-019-0912-1 · Cited by 5,673
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051
Exploring Simple Siamese Representation Learning
Chen, X., & He, K. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01549 · Cited by 3,725
Cited by
Guo, X., Lao, J., Dang, B., Zhang, Y., Yu, L., Ru, L., Zhong, L., Huang, Z., Wu, K., Hu, D., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02613 · Cited by 290
Self-citationNeural Plasticity-Inspired Multimodal Foundation Model for Earth Observation
Xiong, Z., Wang, Y., Zhang, F., Stewart, A. J., Hanna, J., Borth, D., Papoutsis, I., Saux, B. L., Camps-Valls, G., & Zhu, X. X. (2024) · arXiv · doi:10.48550/arxiv.2403.15356 · Cited by 26
Guo, J., Zimmer-Dauphinee, J. R., Nieusma, J. M., Lu, S., Liu, Q., Deng, R., Cui, C., Yue, J., Lin, Y., Yao, T., et al. (2025) · arXiv · doi:10.48550/arxiv.2504.20303 · Cited by 7
Metadata
- Authors
- Hong, D., Zhang, B., Li, X., Li, Y., Li, C., Yao, J., Yokoya, N., Li, H., Ghamisi, P., Jia, X., Plaza, A., Gamba, P., Benediktsson, J. A., & Chanussot, J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2024-08
- Volume
- 46
- Issue
- 8
- Pages
- 5227-5244
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W4393906060
- Cited by
- 930
- References
- 32
- FWCI
- 155.72
- Type
- journal article
- Language
- en
- Open access
- green
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- Hong, D., Zhang, B., Li, X., Li, Y., Li, C., Yao, J., Yokoya, N., Li, H., Ghamisi, P., Jia, X., Plaza, A., Gamba, P., Benediktsson, J. A., & Chanussot, J. (2024). SpectralGPT: Spectral Remote Sensing Foundation Model. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2024.3362475
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