{"url":"/dataset/houston","name":"Houston","full_name":"Houston","description_markdown":"**Houston** is a hyperspectral image classification dataset. The hyperspectral imagery consists of 144 spectral bands in the 380 nm to 1050 nm region and has been calibrated to at-sensor spectral radiance units, SRU =$ \\mu \\text{W} /( \\text{cm}^2 \\text{ sr nm})$. The corresponding co-registered DSM consists of elevation in meters above sea level (per the Geoid 2012A model).","description_withheld":null,"homepage":"https://hyperspectral.ee.uh.edu/?page_id=459","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Hyperspectral images","url":"/datasets/modality/hyperspectral-images"}],"tasks":[{"name":"Hyperspectral Image Classification","url":"/task/hyperspectral-image-classification","datasets_with_task":"/datasets/task/hyperspectral-image-classification"}],"languages":[],"variants":["Houston"],"data_loaders":[{"repo":"https://github.com/songyz2019/rs-fusion-datasets","url":"https://github.com/songyz2019/rs-fusion-datasets","frameworks":["pytorch"]}],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/hyperspectral-image-classification-on-houston","task":"Hyperspectral Image Classification","dataset_variant":"Houston","rows":4,"metrics":["Overall Accuracy","AA@10%perclass","AA@disjoint","F1@10%perclass","Kappa@10%perclass","Kappa@disjoint","OA@10%perclass","OA@15perclass","OA@disjoint"],"first_row_in_archive_order":{"model":"A-SPN","paper":"/paper/attention-based-second-order-pooling-network","metrics":{"Overall Accuracy":"97.27%"},"code_links":[{"title":"ZhaohuiXue/A-SPN-release","url":"https://github.com/ZhaohuiXue/A-SPN-release"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/discrete-cosine-transform-based-joint","title":"Discrete Cosine Transform-Based Joint Spectral-Spatial Information Compression and Band Correlation Calculation for Hyperspectral Feature Extraction","date":"2024-11-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/locality-aware-hyperspectral-classification","title":"Locality-Aware Hyperspectral Classification","date":"2023-09-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adaptive-mask-sampling-and-manifold-to","title":"Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification","date":"2023-04-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/attention-based-second-order-pooling-network","title":"Attention-Based Second-Order Pooling Network for Hyperspectral Image Classification","date":"2021-01-14","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."}