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Awesome Hyperspectral Datasets

A collection of publicly available datasets for hyperspectral imaging research.

Name Year Task URL #Images Size (WxH) #Bands Wavelength Spectral Resolution
Pinot Noir maturity status HS Dataset1 2025 regression https://github.com/hlyu821/GANs 9 - (1800 pixel) 186 406.8-995.8 3.26
HSOD-BIT-V22 2025 salient object detection https://github.com/QYH-BIT/HSOD-BIT-V2?tab=readme-ov-file 500 1240x1680 200 400-1000 3
hyperspectral image datasets-almond, pistachio, and garlic stems3 2024 anomaly detection https://ieee-dataport.org/documents/anomaly-detection-hyperspectral-imaging-food-safety-inspection 12? 400x512 224 400-1000 -
VIS-NIR HSI database D24 2024 - https://github.com/bianlab/Hyperspectral-imaging-dataset 500 640x660 131 400-1700 10
VIS-NIR HSI database D15 2024 - https://github.com/bianlab/Hyperspectral-imaging-dataset 500 960x1230 61 400-1000 10
PENGUIN HS IMAGE DATASET6 2024 classification https://033labcodes.github.io/igrass24_penguin/ 990 2048x1080 151 350-1100 5
Living Optics Orchard Dataset7 2024 segmentation https://huggingface.co/datasets/LivingOptics/hyperspectral-orchard 435 - 96 440-900 -
Living Optics Hyperspectral Fruit Dataset8 2024 classification
segmentation		 https://huggingface.co/datasets/LivingOptics/hyperspectral-fruit 100 - - 440-900? -
Hyperspectral image dataset of unstructured terrains for UGV perception9 2024 classification
segmentation		 https://ieee-dataport.org/documents/hyperspectral-image-dataset-unstructured-terrains-ugv-perception 137 512x512 204 400-1000 7
Hyperspectral Object Tracking Challenge 202410 2024 object tracking https://www.hsitracking.com/ - - - - -
HyperLeaf202411 2024 classification https://www.kaggle.com/competitions/HyperLeaf2024 2410 512x512 204 400-1000 7
Hydrocarbon Spill Hyperspectral Dataset12 2024 classification https://ieee-dataport.org/documents/hydrocarbon-spill-hyperspectral-dataset-hshd 116 1024x1024 20 400-1000 -
DeepHS Debris13 2024 classification https://cogsys.cs.uni-tuebingen.de/webprojects/DeepHS-Debris-2024-Datasets/ 860 - 200? 400-1700 -
CloudPatch-714 2024 classification https://ieee-dataport.org/documents/cloudpatch-7-hyperspectral-dataset 444 50x50 462 400-1000 1.90
Cabbage Eggplant Hyperspectral datasets15 2024 classification https://data.mendeley.com/datasets/whgnf4s4bp/1
https://data.mendeley.com/datasets/t4rysh9rxf/1
https://data.mendeley.com/datasets/cww6zkdcmb/1		 - - - 400-900 3
Beyond RGB16 2024 - https://github.com/shirawerman/Beyond-RGB 1680 2584x1936 16 380-730 -
BJTU-UVA17 2024 calibration https://github.com/duranze/Automatic-spectral-calibration-of-HSI 765 512x512 204 400-1000 3nm
UWA Hyperspectral Face Database18 2023 face recognition https://ieee-dataport.org/documents/uwa-hyperspectral-face-database 120 - 33 400-720 10
MobiSpectral19 2023 hsi reconstruction https://github.com/mobispectral/mobicom23_mobispectral/ 346 512x512 204 400-1000 -
HyperPRI20 2023 segmentation https://github.com/GatorSense/HyperPRI?tab=readme-ov-file 748 - 299 400-1000 2
Hyper-Skin21 2023 hsi reconstruction https://hyper-skin-2023.github.io/ 330 1024x1024 448 400-1000 1.34
HOD3K22 2023 object detection https://github.com/hexiao0275/S2ADet 3242 512x256 16 470-620 -
DeepHS Fruit v223 2023 classification https://github.com/cogsys-tuebingen/deephs_fruit 4671 - - 400-1000/920-1727/408-900 3/3/2
TOHS Dataset24 2022 3D reconstruction https://ieee-dataport.org/documents/tufts-outdoor-hyperspectral-dataset 100 410x410 164 350-1002 4
LIB-HSI25 2022 classification
segmentation		 https://data.csiro.au/collection/csiro%3A55630v4 513 512x512 204 400-1000 -
HSIFoodIngr-6426 2022 classification
segmentation		 https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/E7WDNQ 3389 512x512 204 400-1000 -
HSICityV227 2022 segmentation https://isis-data.science.uva.nl/cv/HyperspectralCityV2.0/ 1330 1889x1422 128 450-950 -
ARAD 1K28 2022 hsi reconstruction https://github.com/boazarad/ARAD_1K?tab=readme-ov-file 1000 482x512 31 400-700 -
Pasta Dataset29 2021 classification
regression		 https://data.mendeley.com/datasets/yhyzmp8rtb/2 50? - - 350-2500 -
OMHS30 2021 hsi reconstruction https://ieee-dataport.org/documents/omhs-objects-mosaic-hyperspectral-database 10666 256x256 29 420-700 -
HSI-131 2021 object detection https://github.com/yanlongbinluck/HSI-Object-Detection-NPU 454 696x860 128 400-1000 0.58
Near Infrared Hyperspectral Image Dataset32 2020 classification https://github.com/hacarus/hsi-open-dataset 96 192x256 - 1300-2150 9.8
Ladybird Cobbitty 2017 Brassica Dataset33 2020 classification
object detection
segmentation		 http://hdl.handle.net/2123/20187 - - - - -
HSI Road34 2020 segmentation https://github.com/NUST-Machine-Intelligence-Laboratory/hsi_road 3799 192x384 25 680-960 -
HFD10035 2020 classification https://github.com/ying-fu/HFD100 10738 696x520 256 376.8-1075.8 2.73
TokyoTech 59-band Visible-NIR Hyperspectral Image Dataset36 2019 - http://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata59.html 16 - 59 400-1000 -
Dataset for Hyperspectral Clinical Applications37 2019 classification https://ieee-dataport.org/open-access/dataset-parallel-implementations-assessment-spatial-spectral-classifier-hyperspectral 3 1000x1000 100 - -
Cocoa beans spectral image38 2019 classification https://ieee-dataport.org/documents/cocoa-beans-spectral-image-three-fermentation-levels - - - - -
HSIDermoscopy39 2018 classification https://github.com/heugyy/HSIDermoscopy - - - - -
HS-SOD (HyperSpectral Salient Object Detection Dataset)40 2018 salient object detection https://github.com/gistairc/HS-SOD?tab=readme-ov-file - - - - -
GHIFVD41 2018 - https://www.allpsych.uni-giessen.de/GHIFVD/ - - - - -
HyKo42 2017 scene understanding https://wp.uni-koblenz.de/hyko/ - - - - -
ICVL43 2016 hsi reconstruction https://huggingface.co/datasets/danaroth/icvl - - - - -
Real-World Hyperspectral Images Database44 2011 - https://vision.seas.harvard.edu/hyperspec/download.html - - - - -
Tecnalia Hyperspectral Dataset45 2010 classification https://zenodo.org/records/12565131 - - - - -
CAVE46 2008 - https://cave.cs.columbia.edu/repository/Multispectral - - - - -

Maintainer Information

This repository is maintained by 033 Laboratory at Tokyo Denki University. The following individuals are responsible for maintaining this repository:

If you have any questions or suggestions, please feel free to contact the maintainers via the provided email address or by openng an issue in this repository.

Acknowledgements

We would like to express our sincere gratitude to the researchers, institutions, and organizations who have contributed to the development and sharing of hyperspectral datasets.

Footnotes

  1. H. Lyu, M. Grafton, T. Ramilan, M. Irwin, and E. Sandoval, “Synthetic hyperspectral reflectance data augmentation by generative adversarial network to enhance grape maturity determination,” Computers and Electronics in Agriculture, vol. 235. Elsevier BV, p. 110341, Aug. 2025. doi: 10.1016/j.compag.2025.110341. ↩

  2. Y. Qiu, S. Bai, T. Xu, P. Liu, H. Qin, and J. Li, "HSOD-BIT-V2: A Challenging Benchmark for Hyperspectral Salient Object Detection," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 6, pp. 6630–663 ↩

  3. J. Lee, M. Kim, J. Yoon, K. Yoo and S.-J. Byun, "Anomaly detection with hyperspectral imaging for food safety inspection", 2024. ↩

  4. Bian, L., Wang, Z., Zhang, Y. et al. A broadband hyperspectral image sensor with high spatio-temporal resolution. Nature 635, 73–81 (2024). do10.1038/s41586-024-08109-1 ↩

  5. Bian, L., Wang, Z., Zhang, Y. et al. A broadband hyperspectral image sensor with high spatio-temporal resolution. Nature 635, 73–81 (2024). do10.1038/s41586-024-08109-1 ↩

  6. Y. Noboru, Y. Ozasa, and M. Tanaka, “Hyperspectral Image Dataset for Individual Penguin Identification,” IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp. 9383–9387, Jul. 07, 2024. doi: 10.1109/igarss53475.2024.10642522. ↩

  7. S. Cho, E. Sheppard, E. Castello, A. Spanellis, D. Pearce, and S. Chappell, "A case study on the integration of a snapshot hyperspectral field-portable imager solving fruit quality assessment," in Photonic Instrumentation Engineering XII, vol. 13373, pp. 15–46, SPIE, Mar. 2025. ↩

  8. Living Optics, "hyperspectral-fruit," Hugging Face, 2024. [Online]. Available: https://huggingface.co/datasets/LivingOptics/hyperspectral-fruit. [Accessed: Apr. 22, 2025]. ↩

  9. Dhanushka Liyanage, Mart Tamre, Robert Hudjakov, February 3, 2024, "Hyperspectral image dataset of unstructured terrains for UGV perception", IEEE Dataport, doi: https://dx.doi.org/10.21227/13bf-pa49. ↩

  10. F. Xiong, J. Zhou, C. Wouter, Y. Zhong, G. Pedram, and C. Jocelyn, “The hyperspectral object tracking challenge (HOT2024),” [Online]. Available: https://www.hsitracking.com/, 2024. ↩

  11. W. M. Laprade et al., “HyperLeaf2024 – A Hyperspectral Imaging Dataset for Classification and Regression of Wheat Leaves,” 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1234–1243, Jun. 2024, doi: 10.1109/cvprw63382.2024.00130. ↩

  12. David Rivas-Lalaleo, Carlos Hernandez, December 5, 2024, "Hydrocarbon Spill Hyperspectral Dataset (HSHD", IEEE Dataport, doi: https://dx.doi.org/10.21227/4etm-h961. ↩

  13. Frank, H., Vetter, K., Varga, L.A., Wolff, L., Zell, A. (2025). Hyperspectral Imaging for Characterization of Construction Waste Material in Recycling Applications. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15316. Springer, Cham. https://doi.org/10.1007/978-3-031-78444-6_11 ↩

  14. Hua Yan, Rachel Zheng, Shivaji Mallela, Brandon Boehm, Sameer Shaga, Derienne Black, Luis Cueva Parra, Randy Russell, Olcay Kursun, May 18, 2024, "CloudPatch-7 Hyperspectral Dataset", IEEE Dataport, doi: https://dx.doi.org/10.21227/fgb9-qs51. ↩

  15. V. K. Munipalle, U. R. Nelakuditi, M. K. C．V．S．S．, and R. R. Nidamanuri, “Ultra-high-resolution hyperspectral imagery datasets for precision agriculture applications,” Data in Brief, vol. 55. Elsevier BV, p. 110649, Aug. 2024. doi: 10.1016/j.dib.2024.110649. ↩

  16. O. Glatt et al., "Beyond RGB: A Real World Dataset for Multispectral Imaging in Mobile Devices," 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2024, pp. 4332-4342, doi: 10.1109/WACV57701.2024.00429. ↩

  17. Z. Du, S. You, C. Cheng, and S. Wei, “Automatic Spectral Calibration of Hyperspectral Images: Method, Dataset and Benchmark,” arXiv preprint arXiv:2412.14925, 2024. [Online]. Available: https://arxiv.org/abs/2412.14925 ↩

  18. Muhammad Uzair, Zohaib Khan, Arif Mahmood, Faisal Shafait, Ajmal Mian, March 28, 2023, "UWA Hyperspectral Face Database", IEEE Dataport, doi: https://dx.doi.org/10.21227/8714-kx37. ↩

  19. N. Sharma, M. S. Waseem, S. Mirzaei, and M. Hefeeda, “MobiSpectral: Hyperspectral Imaging on Mobile Devices,” in Proc. 29th Annu. Int. Conf. Mobile Computing and Networking (ACM MobiCom), Madrid, Spain, 2023, doi: 10.1145/3570361.3613296. ↩

  20. S. J. Chang et al., “HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study.” Harvard Dataverse, 2023. doi: doi:10.7910/DVN/MAYDHT.  ↩

  21. Pai Chet Ng, Zhixiang Chi, Yannick Verdie, Juwei Lu, and Konstantinos N. Plataniotis, "Hyper-Skin: a hyperspectral dataset for reconstructing facial skin-spectra from RGB images," Proceedings of the 37th International Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, USA, 2023, Art. no. 1050, pp. 1-13. ↩

  22. X. He, C. Tang, X. Liu, W. Zhang, K. Sun and J. Xu, "Object Detection in Hyperspectral Image via Unified Spectral–Spatial Feature Aggregation," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-13, 2023, Art no. 5521213, doi: 10.1109/TGRS.2023.3307288.  ↩

  23. L. A. Varga, J. Makowski and A. Zell, "Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning," 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, 2021, pp. 1-8, doi: 10.1109/IJCNN52387.2021.9533728. ↩

  24. A. Stone, S. P. Rao, S. Rajeev, K. Panetta and S. Agaian, "A Comprehensive 2D + 3D Dataset for Benchmarking Hyperspectral Imaging Systems," 2022 IEEE International Symposium on Technologies for Homeland Security (HST), Boston, MA, USA, 2022, pp. 1-5, doi: 10.1109/HST56032.2022.10024982. ↩

  25. N. Habili, E. Kwan, W. Li, C. Webers, J. Oorloff, M. A. Armin, and L. Petersson, “A hyperspectral and RGB dataset for building façade segmentation,” in Proc. ECCV 2022 Workshops, Tel Aviv, Israel, Oct. 23–27, 2022, Part VII, pp. 258–267, Springer, 2023. ↩

  26. X. Xia, W. Liu, L. Wang and J. Sun, "HSIFoodIngr-64: A Dataset for Hyperspectral Food-Related Studies and a Benchmark Method on Food Ingredient Retrieval," in IEEE Access, vol. 11, pp. 13152-13162, 2023, doi: 10.1109/ACCESS.2023.3243243.  ↩

  27. Y. Huang, T. Ren, Q. Shen, Y. Fu, and S. You, “HSICityV2: Urban Scene Understanding via Hyperspectral Images,” Zenodo, 2021. [Online]. Available: https://doi.org/10.5281/zenodo.703085 ↩

  28. B. Arad et al., "NTIRE 2022 Spectral Recovery Challenge and Data Set," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, LA, USA, 2022, pp. 862-880, doi: 10.1109/CVPRW56347.2022.00102. ↩

  29. Bonifazi, Giuseppe; Gasbarrone, Riccardo; Capobianco, Giuseppe; Serranti, Silvia (2021), “A dataset of Visible – Short Wave InfraRed reflectance spectra collected on pre-cooked pasta products”, Mendeley Data, V2, doi: 10.17632/yhyzmp8rtb.2 ↩

  30. Jonathan Hauser, Gal Shtendel, Amit Zeligman, Amir Averbuch, Menachem Nathan, Moshe Salhov, May 5, 2021, "OMHS - The Objects Mosaic Hyperspectral Database", IEEE Dataport, doi: https://dx.doi.org/10.21227/36g6-r506. ↩

  31. L. Yan, M. Zhao, X. Wang, Y. Zhang and J. Chen, "Object Detection in Hyperspectral Images," in IEEE Signal Processing Letters, vol. 28, pp. 508-512, 2021, doi: 10.1109/LSP.2021.3059204. ↩

  32. hacarus, “GitHub - hacarus/hsi-open-dataset,” GitHub, 2020. https://github.com/hacarus/hsi-open-dataset (accessed Apr. 19, 2025). ↩

  33. A. Bender, B. Whelan, and S. Sukkarieh, “A high-resolution, multimodal data set for agricultural robotics: A Ladybird's-eye view of Brassica,” J. Field Robot., vol. 37, no. 1, pp. 73–96, 2020, doi: 10.1002/rob.21877. ↩

  34. J. Lu, H. Liu, Y. Yao, S. Tao, Z. Tang and J. Lu, "Hsi Road: A Hyper Spectral Image Dataset For Road Segmentation," 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK, 2020, pp. 1-6, doi: 10.1109/ICME46284.2020.9102890. ↩

  35. Y. Zheng, T. Zhang, and Y. Fu, “A large-scale hyperspectral dataset for flower classification,” Knowledge-Based Systems, vol. 236. Elsevier BV, p. 107647, Jan. 2022. doi: 10.1016/j.knosys.2021.107647. ↩

  36. Y. Monno, H. Teranaka, K. Yoshizaki, M. Tanaka, and M. Okutomi, “Single-sensor RGB-NIR imaging: High-quality system design and prototype implementation,” IEEE Sens. J., vol. 19, no. 2, pp. 497–507, 2018. ↩

  37. Himar Fabelo, Samuel Ortega, Raquel León, Gustavo Callico, August 28, 2019, "Dataset: Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications", IEEE Dataport, doi: https://dx.doi.org/10.21227/pn25-nj87. ↩

  38. Carlos Hinojosa, Karen Sanchez, Hans Garcia, Henry Arguello, December 10, 2019, "Cocoa beans spectral image with three fermentation levels", IEEE Dataport, doi: https://dx.doi.org/10.21227/esks-4b74. ↩

  39. Y. Gu, Y.-P. Partridge, and J. Zhou, “A Hyperspectral Dermoscopy Dataset for Melanoma Detection,” Lecture Notes in Computer Science. Springer International Publishing, pp. 268–276, 2018. doi: 10.1007/978-3-030-01201-4_29. ↩

  40. N. Imamoglu, Y. Oishi, X. Zhang, G. Ding, Y. Fang, T. Kouyama, and R. Nakamura, “Hyperspectral image dataset for benchmarking on salient object detection,” in Proc. 10th Int. Conf. Quality of Multimedia Experience (QoMEX), 2018, pp. 1–3, doi: 10.1109/QoMEX.2018.8463428. ↩

  41. R. Ennis, F. Schiller, M. Toscani, and K. R. Gegenfurtner, “Hyperspectral database of fruits and vegetables,” Journal of the Optical Society of America A, vol. 35, no. 4. Optica Publishing Group, p. B256, Mar. 14, 2018. doi: 10.1364/josaa.35.00b256. ↩

  42. C. Winkens, F. Sattler, V. Adams and D. Paulus, “HyKo: A Spectral Dataset for Scene Understanding,” 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), Venice, 2017, pp. 254-261. ↩

  43. B. Arad and O. Ben-Shahar, “Sparse Recovery of Hyperspectral Signal from Natural RGB Images,” Computer Vision – ECCV 2016, pp. 19–34, 2016, doi: https://doi.org/10.1007/978-3-319-46478-7_2. ↩

  44. A. Chakrabarti and T. Zickler, "Statistics of real-world hyperspectral images," CVPR 2011, Colorado Springs, CO, USA, 2011, pp. 193-200, doi: 10.1109/CVPR.2011.5995660. ↩

  45. A. Picon, O. Ghita, P. M. Iriondo, A. Bereciartua and P. F. Whelan, "Automation of waste recycling using hyperspectral image analysis," 2010 IEEE 15th Conference on Emerging Technologies & Factory Automation (ETFA 2010), Bilbao, Spain, 2010, pp. 1-4, doi: 10.1109/ETFA.2010.5641201. ↩

  46. F. Yasuma, T. Mitsunaga, D. Iso and S. K. Nayar, "Generalized Assorted Pixel Camera: Postcapture Control of Resolution, Dynamic Range, and Spectrum," in IEEE Transactions on Image Processing, vol. 19, no. 9, pp. 2241-2253, Sept. 2010, doi: 10.1109/TIP.2010.2046811. ↩

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