{"id":28758271,"url":"https://github.com/033labcodes/awesome-hyperspectral-datasets","last_synced_at":"2025-06-17T04:06:42.903Z","repository":{"id":288854210,"uuid":"964541447","full_name":"033labcodes/awesome-hyperspectral-datasets","owner":"033labcodes","description":"Awesome datasets for hyperspectral imaging research.","archived":false,"fork":false,"pushed_at":"2025-05-06T13:07:35.000Z","size":639,"stargazers_count":13,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-13T14:22:08.231Z","etag":null,"topics":["awesome","hyperspectral","hyperspectral-data","hyperspectral-datasets","hyperspectral-imaging"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/033labcodes.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-04-11T11:29:18.000Z","updated_at":"2025-05-08T16:59:11.000Z","dependencies_parsed_at":null,"dependency_job_id":"7ea472bc-9b41-477e-8566-4b9f8d0cff5a","html_url":"https://github.com/033labcodes/awesome-hyperspectral-datasets","commit_stats":null,"previous_names":["033labcodes/awesome-hyperspectral-datasets"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/033labcodes/awesome-hyperspectral-datasets","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/033labcodes%2Fawesome-hyperspectral-datasets","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/033labcodes%2Fawesome-hyperspectral-datasets/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/033labcodes%2Fawesome-hyperspectral-datasets/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/033labcodes%2Fawesome-hyperspectral-datasets/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/033labcodes","download_url":"https://codeload.github.com/033labcodes/awesome-hyperspectral-datasets/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/033labcodes%2Fawesome-hyperspectral-datasets/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260288471,"owners_count":22986667,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["awesome","hyperspectral","hyperspectral-data","hyperspectral-datasets","hyperspectral-imaging"],"created_at":"2025-06-17T04:06:42.193Z","updated_at":"2025-06-17T04:06:42.861Z","avatar_url":"https://github.com/033labcodes.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Awesome Hyperspectral Datasets\n\n\u003e A collection of publicly available datasets for hyperspectral imaging research.\n\n| Name | Year | Task | URL |\n| --- | --- | --- | --- |\n| Pinot Noir maturity status HS Dataset[^1] | 2025 | regression | [https://github.com/hlyu821/GANs](https://github.com/hlyu821/GANs) |\n| HSOD-BIT-V2[^2] | 2025 | salient object detection | [https://github.com/QYH-BIT/HSOD-BIT-V2?tab=readme-ov-file](https://github.com/QYH-BIT/HSOD-BIT-V2?tab=readme-ov-file) |\n| CloudPatch-7[^3] | 2024 | classification | [https://ieee-dataport.org/documents/cloudpatch-7-hyperspectral-dataset](https://ieee-dataport.org/documents/cloudpatch-7-hyperspectral-dataset) |\n| PENGUIN HS IMAGE DATASET[^4] | 2024 | classification | [https://033labcodes.github.io/igrass24_penguin/](https://033labcodes.github.io/igrass24_penguin/) |\n| Hyperspectral Object Tracking Challenge 2024[^5] | 2024 | object tracking | [https://www.hsitracking.com/](https://www.hsitracking.com/) |\n| VIS-NIR HSI dataset[^6] | 2024 |  | [https://github.com/bianlab/Hyperspectral-imaging-dataset](https://github.com/bianlab/Hyperspectral-imaging-dataset) |\n| HyperLeaf2024[^7] | 2024 | classification | [https://www.kaggle.com/competitions/HyperLeaf2024](https://www.kaggle.com/competitions/HyperLeaf2024) |\n| Cabbage Eggplant Hyperspectral datasets[^8] | 2024 | classification | [https://data.mendeley.com/datasets/whgnf4s4bp/1](https://data.mendeley.com/datasets/whgnf4s4bp/1)\u003cbr\u003e[https://data.mendeley.com/datasets/t4rysh9rxf/1](https://data.mendeley.com/datasets/t4rysh9rxf/1)\u003cbr\u003e[https://data.mendeley.com/datasets/cww6zkdcmb/1](https://data.mendeley.com/datasets/cww6zkdcmb/1) |\n| BJTU-UVA[^9] | 2024 | calibration | [https://github.com/duranze/Automatic-spectral-calibration-of-HSI](https://github.com/duranze/Automatic-spectral-calibration-of-HSI) |\n| Living Optics Orchard Dataset[^10] | 2024 | segmentation | [https://huggingface.co/datasets/LivingOptics/hyperspectral-orchard](https://huggingface.co/datasets/LivingOptics/hyperspectral-orchard) |\n| hyperspectral image datasets-almond, pistachio, and garlic stems[^11] | 2024 | anomaly detection | [https://ieee-dataport.org/documents/anomaly-detection-hyperspectral-imaging-food-safety-inspection](https://ieee-dataport.org/documents/anomaly-detection-hyperspectral-imaging-food-safety-inspection) |\n| Hydrocarbon Spill Hyperspectral Dataset[^12] | 2024 | classification | [https://ieee-dataport.org/documents/hydrocarbon-spill-hyperspectral-dataset-hshd](https://ieee-dataport.org/documents/hydrocarbon-spill-hyperspectral-dataset-hshd) |\n| Hyperspectral image dataset of unstructured terrains for UGV perception[^13] | 2024 | classification\u003cbr\u003esegmentation | [https://ieee-dataport.org/documents/hyperspectral-image-dataset-unstructured-terrains-ugv-perception](https://ieee-dataport.org/documents/hyperspectral-image-dataset-unstructured-terrains-ugv-perception) |\n| Living Optics Hyperspectral Fruit Dataset[^14] | 2024 | classification\u003cbr\u003esegmentation | [https://huggingface.co/datasets/LivingOptics/hyperspectral-fruit](https://huggingface.co/datasets/LivingOptics/hyperspectral-fruit) |\n| DeepHS Debris[^15] | 2024 | classification | [https://cogsys.cs.uni-tuebingen.de/webprojects/DeepHS-Debris-2024-Datasets/](https://cogsys.cs.uni-tuebingen.de/webprojects/DeepHS-Debris-2024-Datasets/) |\n| HyperPRI[^16] | 2023 | segmentation | [https://github.com/GatorSense/HyperPRI?tab=readme-ov-file](https://github.com/GatorSense/HyperPRI?tab=readme-ov-file) |\n| HOD3K[^17] | 2023 | object detection | [https://github.com/hexiao0275/S2ADet](https://github.com/hexiao0275/S2ADet) |\n| UWA Hyperspectral Face Database[^18] | 2023 | face recognition | [https://ieee-dataport.org/documents/uwa-hyperspectral-face-database](https://ieee-dataport.org/documents/uwa-hyperspectral-face-database) |\n| Hyper-Skin[^19] | 2023 | hsi reconstruction | [https://hyper-skin-2023.github.io/](https://hyper-skin-2023.github.io/) |\n| DeepHS Fruit v2[^20] | 2023 | classification | [https://github.com/cogsys-tuebingen/deephs_fruit](https://github.com/cogsys-tuebingen/deephs_fruit) |\n| MobiSpectral[^21] | 2023 | hsi reconstruction | [https://github.com/mobispectral/mobicom23_mobispectral/](https://github.com/mobispectral/mobicom23_mobispectral/) |\n| HSICityV2[^22] | 2022 | segmentation | [https://isis-data.science.uva.nl/cv/HyperspectralCityV2.0/](https://isis-data.science.uva.nl/cv/HyperspectralCityV2.0/) |\n| LIB-HSI (the Light Industrial Building HS)[^23] | 2022 | classification\u003cbr\u003esegmentation | [https://data.csiro.au/collection/csiro%3A55630v4](https://data.csiro.au/collection/csiro%3A55630v4) |\n| HFD100[^24] | 2022 | classification | [https://github.com/ying-fu/HFD100](https://github.com/ying-fu/HFD100) |\n| HSIFoodIngr-64[^25] | 2022 | classification\u003cbr\u003esegmentation | [https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/E7WDNQ](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/E7WDNQ) |\n| ARAD 1K[^26] | 2022 | hsi reconstruction | [https://github.com/boazarad/ARAD_1K?tab=readme-ov-file](https://github.com/boazarad/ARAD_1K?tab=readme-ov-file) |\n| TOHS Dataset[^27] | 2022 | 3D reconstruction | [https://ieee-dataport.org/documents/tufts-outdoor-hyperspectral-dataset](https://ieee-dataport.org/documents/tufts-outdoor-hyperspectral-dataset) |\n| Pasta Dataset[^28] | 2021 | classification\u003cbr\u003eregression | [https://data.mendeley.com/datasets/yhyzmp8rtb/2](https://data.mendeley.com/datasets/yhyzmp8rtb/2) |\n| HSI-1[^29] | 2021 | object detection | [https://github.com/hexiao0275/S2ADet](https://github.com/hexiao0275/S2ADet) |\n| OMHS[^30] | 2021 | hsi reconstruction | [https://ieee-dataport.org/documents/omhs-objects-mosaic-hyperspectral-database](https://ieee-dataport.org/documents/omhs-objects-mosaic-hyperspectral-database) |\n| HSI-Object-Detection-NPU[^31] | 2021 | object detection | [https://github.com/yanlongbinluck/HSI-Object-Detection-NPU](https://github.com/yanlongbinluck/HSI-Object-Detection-NPU) |\n| Ladybird Cobbitty 2017 Brassica Dataset[^32] | 2020 | classification\u003cbr\u003eobject detection\u003cbr\u003esegmentation | [http://hdl.handle.net/2123/20187](http://hdl.handle.net/2123/20187) |\n| HSI Road[^33] | 2020 | segmentation | [https://github.com/NUST-Machine-Intelligence-Laboratory/hsi_road](https://github.com/NUST-Machine-Intelligence-Laboratory/hsi_road) |\n| Near Infrared Hyperspectral Image Dataset[^34] | 2020 | classification | [https://github.com/hacarus/hsi-open-dataset](https://github.com/hacarus/hsi-open-dataset) |\n| TokyoTech 59-band Visible-NIR Hyperspectral Image Dataset[^35] | 2019 |  | [http://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata59.html](http://www.ok.sc.e.titech.ac.jp/res/MSI/MSIdata59.html) |\n| Dataset for Hyperspectral Clinical Applications[^36] | 2019 | classification | [https://ieee-dataport.org/open-access/dataset-parallel-implementations-assessment-spatial-spectral-classifier-hyperspectral](https://ieee-dataport.org/open-access/dataset-parallel-implementations-assessment-spatial-spectral-classifier-hyperspectral) |\n| Cocoa beans spectral image[^37] | 2019 | classification | [https://ieee-dataport.org/documents/cocoa-beans-spectral-image-three-fermentation-levels](https://ieee-dataport.org/documents/cocoa-beans-spectral-image-three-fermentation-levels) |\n| GHIFVD[^38] | 2018 |  | [https://www.allpsych.uni-giessen.de/GHIFVD/](https://www.allpsych.uni-giessen.de/GHIFVD/) |\n| HSIDermoscopy[^39] | 2018 | classification | [https://github.com/heugyy/HSIDermoscopy](https://github.com/heugyy/HSIDermoscopy) |\n| HS-SOD (HyperSpectral Salient Object Detection Dataset)[^40] | 2018 | salient object detection | [https://github.com/gistairc/HS-SOD?tab=readme-ov-file](https://github.com/gistairc/HS-SOD?tab=readme-ov-file) |\n| HyKo[^41] | 2017 | scene understanding | [https://wp.uni-koblenz.de/hyko/](https://wp.uni-koblenz.de/hyko/) |\n| ICVL[^42] | 2016 | hsi reconstruction | [https://huggingface.co/datasets/danaroth/icvl](https://huggingface.co/datasets/danaroth/icvl) |\n| Real-World Hyperspectral Images Database[^43] | 2011 |  | [https://vision.seas.harvard.edu/hyperspec/download.html](https://vision.seas.harvard.edu/hyperspec/download.html) |\n| Tecnalia Hyperspectral Dataset[^44] | 2010 | classification | [https://zenodo.org/records/12565131](https://zenodo.org/records/12565131) |\n| CAVE[^45] | 2008 |  | [https://cave.cs.columbia.edu/repository/Multispectral](https://cave.cs.columbia.edu/repository/Multispectral) |\n\n[^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.\n\n\n[^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\n\n\n[^3]: 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.\n\n[^4]: 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.\n\n[^5]: 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.\n\n[^6]: 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\n\n[^7]: 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.\n\n[^8]: 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.\n\n\n[^9]: 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\n\n[^10]: 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.\n\n[^11]: J. Lee, M. Kim, J. Yoon, K. Yoo and S.-J. Byun, \"Anomaly detection with hyperspectral imaging for food safety inspection\", 2024.\n\n[^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.\n\n[^13]: 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.\n\n[^14]: Living Optics, \"hyperspectral-fruit,\" Hugging Face, 2024. [Online]. Available: https://huggingface.co/datasets/LivingOptics/hyperspectral-fruit. [Accessed: Apr. 22, 2025].\n\n\n[^15]: 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\n\n[^16]: 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. \n\n[^17]: 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. \n\n[^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.\n\n[^19]: 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.\n\n[^20]: 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.\n\n[^21]: 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.\n\n[^22]: 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\n\n[^23]: 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.\n\n[^24]: 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.\n\n\n[^25]: 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. \n\n[^26]: 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.\n\n[^27]: 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.\n\n[^28]: 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\n\n[^29]: 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.\n\n[^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.\n\n[^31]: L. Yan, M. Zhao, X. Wang, Y. Zhang, and J. Chen, “Object detection in hyperspectral images,” IEEE Signal Process. Lett., vol. 28, pp. 508–512, 2021.\n\n[^32]: 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.\n\n[^33]: 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.\n\n[^34]: hacarus, “GitHub - hacarus/hsi-open-dataset,” GitHub, 2020. https://github.com/hacarus/hsi-open-dataset (accessed Apr. 19, 2025).\n\n[^35]: 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.\n\n[^36]: 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.\n\n[^37]: 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.\n\n[^38]: 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.\n\n\n[^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.\n\n\n[^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.\n\n[^41]: 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.\n\n[^42]: 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.\n\n[^43]: 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.\n\n[^44]: 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 \u0026 Factory Automation (ETFA 2010), Bilbao, Spain, 2010, pp. 1-4, doi: 10.1109/ETFA.2010.5641201.\n\n[^45]: 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.\n\n## Maintainer Information\nThis repository is maintained by **[033 Laboratory](https://033lab.org/#)** at Tokyo Denki University. \nThe following individuals are responsible for maintaining this repository:\n\n- **Keita Ogawa**\n    -  Email: 25amj08@ms.dendai.ac.jp\n\n- **Youta Noboru**\n    -  Email: 24amj29@ms.dendai.ac.jp   \n\nIf 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.\n\n## Acknowledgements\nWe would like to express our sincere gratitude to the researchers, institutions, and organizations who have contributed to the development and sharing of hyperspectral datasets. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F033labcodes%2Fawesome-hyperspectral-datasets","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F033labcodes%2Fawesome-hyperspectral-datasets","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F033labcodes%2Fawesome-hyperspectral-datasets/lists"}