{"url":"/dataset/salinas","name":"Salinas","full_name":"Salinas Scene","description_markdown":"**Salinas Scene** is a hyperspectral dataset collected by the 224-band AVIRIS sensor over Salinas Valley, California, and is characterized by high spatial resolution (3.7-meter pixels). The area covered comprises 512 lines by 217 samples. 20 water absorption bands were discarder: [108-112], [154-167], 224. This image was available only as at-sensor radiance data. It includes vegetables, bare soils, and vineyard fields. Salinas groundtruth contains 16 classes.","description_withheld":null,"homepage":"http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Hyperspectral images","url":"/datasets/modality/hyperspectral-images"}],"tasks":[{"name":"Semi-Supervised Image Classification","url":"/task/semi-supervised-image-classification","datasets_with_task":"/datasets/task/semi-supervised-image-classification"},{"name":"Hyperspectral Image Classification","url":"/task/hyperspectral-image-classification","datasets_with_task":"/datasets/task/hyperspectral-image-classification"}],"languages":[],"variants":["Salinas Scene","Salinas"],"data_loaders":[],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/hyperspectral-image-classification-on-salinas","task":"Hyperspectral Image Classification","dataset_variant":"Salinas Scene","rows":3,"metrics":["Overall Accuracy"],"first_row_in_archive_order":{"model":"HybridSN","paper":"/paper/hybridsn-exploring-3d-2d-cnn-feature","metrics":{"Overall Accuracy":"100%"},"code_links":[{"title":"gokriznastic/HybridSN","url":"https://github.com/gokriznastic/HybridSN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/hyperspectral-image-classification-on-salinas-1","task":"Hyperspectral Image Classification","dataset_variant":"Salinas","rows":3,"metrics":["OA@200","AA@200","Kappa@200","Overall Accuracy"],"first_row_in_archive_order":{"model":"JigsawHSI","paper":"/paper/jigsawhsi-a-network-for-hyperspectral-image","metrics":{"OA@200":"100.00"},"code_links":[{"title":"jmoraga-mines/jigsawhsi","url":"https://github.com/jmoraga-mines/jigsawhsi"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semi-supervised-image-classification-on-17","task":"Semi-Supervised Image Classification","dataset_variant":"Salinas","rows":1,"metrics":["Overall Accuracy"],"first_row_in_archive_order":{"model":"Res-CP","paper":"/paper/semi-supervised-hyperspectral-image-1","metrics":{"Overall Accuracy":"97.24"},"code_links":[{"title":"majidseydgar/Res-CP","url":"https://github.com/majidseydgar/Res-CP"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hyperspectral-image-classification-using-deep","title":"Hyperspectral Image Classification Using Deep Matrix Capsules","date":"2023-02-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semi-supervised-hyperspectral-image-1","title":"Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework","date":"2022-08-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/jigsawhsi-a-network-for-hyperspectral-image","title":"JigsawHSI: a network for Hyperspectral Image classification","date":"2022-06-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/faster-hyperspectral-image-classification","title":"Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks","date":"2022-02-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spectralnet-exploring-spatial-spectral","title":"SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification","date":"2021-04-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/fpga-fast-patch-free-global-learning-1","title":"FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification","date":"2020-11-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hybridsn-exploring-3d-2d-cnn-feature","title":"HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification","date":"2019-02-18","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}