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A versatile deep learning architecture for classification and label-free prediction of hyperspectral images
Open accessAbstract
Hyperspectral imaging is a technique that provides rich chemical or compositional information not regularly available to traditional imaging modalities such as intensity imaging or color imaging based on the reflection, transmission, or emission of light. Analysis of hyperspectral imaging often relies on machine learning methods to extract information. Here, we present a new flexible architecture, the U-within-U-Net, that can perform classification, segmentation, and prediction of orthogonal imaging modalities on a variety of hyperspectral imaging techniques. Specifically, we demonstrate feature segmentation and classification on the Indian Pines hyperspectral dataset and simultaneous location prediction of multiple drugs in mass spectrometry imaging of rat liver tissue. We further demonstrate label-free fluorescence image prediction from hyperspectral stimulated Raman scattering microscopy images. The applicability of the U-within-U-Net architecture on diverse datasets with widely varying input and output dimensions and data sources suggest that it has great potential in advancing the use of hyperspectral imaging across many different application areas ranging from remote sensing, to medical imaging, to microscopy.
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“Finally, we remark that our unsupervised autoencoder models do not utilize any ground truth information during training, in contrast with previous supervised machine learning approaches for Raman cell imaging and characterization where specific target concentrations are used as labels to train the model (e.g., using separately acquired fluorescence images) (90–93).”
From Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders (2024) · cites this paper as [90]
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MethodADMM-DSP: A Deep Spectral Image Prior for Snapshot Spectral Image Demosaicing
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BackgroundAdaptive Center-Focused Hybrid Attention Network for Change Detection in Hyperspectral Images
Jiang, F., Zhang, S., Zhang, M., Gong, M., Zhou, Y., Zhao, W. Z., & Guan, Z. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3534881 · Cited by 11
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Unmixing Autoencoder for Image Reconstruction from Hyperspectral Data
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“Experiments have shown that UwU-net architectures outperform more generic U-Nets on supervised image-to-image regression tasks involving HSI data [14].”
Influential2025 photonics for agrifood roadmap: towards a sustainable and healthier planet
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“In many application fields, semantic segmentation or pixel classification is also referred to as pixel-based image classification or simply image classification [13], [14], [15], [16], [17], [18].”
MethodWan, Y., & Zhong, Y. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3403648 · Cited by 8
“Thus, through the subtle differences between HSI spectral bands, it can finely distinguish ground objects [4].”
BackgroundYu, H., Srisuma, P., Devos, C., Wang, J., Myerson, A. S., & Braatz, R. D. (2025) · Advanced Science · doi:10.1002/advs.202508506 · Cited by 8
The power of fusion: LiDAR meets hyperspectral imaging in a new era of exploration
Kazanskiy, N. L., Doskolovich, L. L., Владимирович, Г. Н., & Khonina, S. N. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.114080 · Cited by 10
Sherin, Z., Bashir, O., Mondal, I. H., & Pawase, P. A. (2026) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2026.105558 · Cited by 8
RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction
Zhou, D., Gu, C., Xu, J., Liu, F., Wang, Q., Chen, G., & Heng, P. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00323 · Cited by 10
Enhancing hyperspectral imaging
Bhargava, R., & Falahkheirkhah, K. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00336-9 · Cited by 8
Sun, B., Wang, Z., Lin, J., Chen, C., Zheng, G., Yue, S., Wang, H., Kang, X., Chen, X., Hong, W., et al. (2022) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2022.100114 · Cited by 8
Zhang, R., Su, J., & Feng, J. (2023) · Evolutionary Intelligence · doi:10.1007/s12065-023-00852-0 · Cited by 8
“In recent years, machine learning, as an important branch of artificial intelligence, has been widely used in feature selection [1, 2], natural language processing [3], image detection [4], image classification [5], data mining [6], and other fields.”
Viewing 3D spatial biology with highly-multiplexed Raman imaging: from spectroscopy to biotechnology
Li, Y., Sun, Y., & Shi, L. (2024) · Chemical Communications · doi:10.1039/d4cc02319f · Cited by 7
Hu, Y., Du, Q., Luo, J., Yu, C., Zhao, B., & Sun, Y. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010127 · Cited by 7
Chen, P., Sun, H., Ming, Z., Tian, Y., & Zhang, Z. (2025) · ACS Nano · doi:10.1021/acsnano.5c04778 · Cited by 7
García-Peraza-Herrera, L. C., Horgan, C. C., Sebastien Ourselin, A., Ebner, M., & Vercauteren, T. K. (2023) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2022.2160377 · Cited by 7
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“Hyperspectral target detection and classification algorithms [7], [8], [9], [10], [11], [12] have found widespread applications in different areas: medical diagnosis [13], mineral detection [14], water quality monitoring [15], and crop yield optimization [16] are only some examples of the current…”
BackgroundWei, Z., Liu, W., Yu, W., Liu, X., Yan, R., Liu, Q., & Guo, Q. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231810827 · Cited by 7
“These data sets were acquired simultaneously using image software by collecting the SRS signals from lock-in amplifiers and fluorescence signals from photomultiplier tubes [36].”
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Advances in hyperspectral imaging-based non-destructive assessment of tomato quality attributes
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AI Technologies in Agriculture: Transforming Farming with Intelligent Solutions
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Methodologies for Assessing Chemical Toxicity to Aquatic Microorganisms: A Comparative Review
Chen, H., Li, Y., Quanzhan, C., Chen, C., & 胡耀娟 (2026) · Molecules · doi:10.3390/molecules31030485 · Cited by 6
“Hyperspectral imaging (HSI) integrates spectral and spatial imaging to simultaneously capture high-resolution spectral data for each pixel along with visual information, thereby correlating a sample’s morphology with its chemical composition [52,53].”
Klein, N., Carr, A., Hampel-Arias, Z., Zastrow, A., Ziemann, A., & FLYNN, E. B. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10283350 · Cited by 7
“Machine learning for HSI Recent works have explored deep learning for HSI [6, 3, 5], often with a focus on per-pixel classification, though extensions to semantic segmentation have been explored [8].”
BackgroundUnsupervised Range-Nullspace Learning Prior for Multispectral Images Reconstruction
Chen, Y., Wang, Y., & Zhang, H. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3560430 · Cited by 5
“SI holds great potential in various domains, including remote sensing [1], [2], medical diagnosis [3], and face detection [4].”
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Methods for Rapid Evaluation of Microbial Antibiotics Resistance
Yabbarov, N. G., Nikolskaya, E. D., Bibikov, S. B., Maltsev, A. A., Chirkina, M., Mollaeva, M. R., Sokol, M. B., Epova, E. Y., Aliev, R. O., & Kurochkin, I. N. (2025) · Biochemistry (Moscow) · doi:10.1134/s0006297924603678 · Cited by 4
Hyperspectral Information Extraction With Full Resolution From Arbitrary Photographs
Kwon, S., Park, S. M., Ji, Y., Sakthivel, H., Leem, J. W., & Kim, Y. L. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3597038 · Cited by 4
“Recently, machine-learning approaches have rejuvenated this classically hardware-driven field [16], [17], [18], [19], [20], [21].”
Metadata
- Authors
- Manifold, B., Men, S., Hu, R., & Fu, D.
- Affiliations
- University of Washington (US)
- Corresponding author
- Manifold, Bryce
- Fu, Dan
- Venue
- Nature Machine Intelligence
- Published
- 2021-03-11
- Volume
- 3
- Issue
- 4
- Pages
- 306-315
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC8528004
- OpenAlex
- W3134761005
- Cited by
- 152
- References
- 70
- Citations by year
- 2026: 16 · 2025: 42 · 2024: 36 · 2023: 30 · 2022: 20 · 2021: 8
- FWCI
- 10.55
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Beckman Young Investigator Award
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- hyperspectral imaging, U-Net architecture, image classification, image segmentation, mass spectrometry imaging, stimulated Raman scattering microscopy, Indian Pines dataset, rat liver tissue, remote sensing
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