{"url":"/dataset/pavia-university","name":"Pavia University","full_name":"Pavia University","description_markdown":"The **Pavia University** dataset is a hyperspectral image dataset which gathered by a sensor known as the reflective optics system imaging spectrometer (ROSIS-3) over the city of Pavia, Italy. The image consists of 610×340 pixels with 115 spectral bands. The image is divided into 9 classes with a total of 42,776 labelled samples, including the asphalt, meadows, gravel, trees, metal sheet, bare soil, bitumen, brick, and shadow.\n\nSource: [Diversity in Machine Learning](https://arxiv.org/abs/1807.01477)\nImage Source: [http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_Centre_and_University](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_Centre_and_University)","description_withheld":null,"homepage":"http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_Centre_and_University","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"Pavia centre and university","first_author":null,"url":"http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_Centre_and_University"},"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":["Pavia University"],"data_loaders":[],"num_papers_in_archive":49,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/hyperspectral-image-classification-on-pavia","task":"Hyperspectral Image Classification","dataset_variant":"Pavia University","rows":33,"metrics":["OA@15perclass","Overall Accuracy","AA@200","Kappa@200","OA@200","AA@5%perclass","Kappa@1%","Kappa@5%perclass","OA@5%perclass","AA@1%perclass","Kappa@1%perclass","OA@1%perclass"],"first_row_in_archive_order":{"model":"RPNet-RF","paper":"/paper/small-sample-hyperspectral-image","metrics":{"OA@15perclass":"95.60"},"code_links":[{"title":"UchaevD/RPNet-RF","url":"https://github.com/UchaevD/RPNet-RF"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/mvnet-hyperspectral-remote-sensing-image","title":"MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture","date":"2025-07-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hyperspectral-image-classification-via","title":"Hyperspectral Image Classification via Transformer-based Spectral-Spatial Attention Decoupling and Adaptive Gating","date":"2025-06-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dynamic-3d-kan-convolution-with-adaptive-grid","title":"Dynamic 3D KAN Convolution with Adaptive Grid Optimization for Hyperspectral Image Classification","date":"2025-04-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/expert-kernel-generation-network-driven-by","title":"Expert Kernel Generation Network Driven by Contextual Mapping for Hyperspectral Image Classification","date":"2025-04-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/3d-wavelet-convolutions-with-extended","title":"3D Wavelet Convolutions with Extended Receptive Fields for Hyperspectral Image Classification","date":"2025-04-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spatial-geometry-enhanced-3d-dynamic-snake","title":"Spatial-Geometry Enhanced 3D Dynamic Snake Convolutional Neural Network for Hyperspectral Image Classification","date":"2025-04-06","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/small-sample-hyperspectral-image","title":"Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering","date":"2023-02-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/exploring-the-relationship-between-center-and","title":"Exploring the Relationship between Center and Neighborhoods: Central Vector oriented Self-Similarity Network for Hyperspectral Image Classification","date":"2022-10-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/generative-adversarial-networks-based-on-1","title":"Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification","date":"2022-07-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/graph-information-aggregation-cross-domain","title":"Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification","date":"2022-06-30","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/a-spectral-spatial-dependent-global-learning","title":"A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification","date":"2021-05-29","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/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},{"paper":"/paper/attention-based-adaptive-spectral-spatial","title":"Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification","date":"2020-12-24","rows_on_this_dataset":1,"code_links":1,"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/hyperspectral-image-classification-of","title":"Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples","date":"2020-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/generative-adversarial-networks-based-on-2","title":"Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification","date":"2020-04-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-metric-learning-based-feature-embedding","title":"Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification","date":"2019-10-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hsi-bert-hyperspectral-image-classification","title":"HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers","date":"2019-09-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-learning-for-classification-of-1","title":"Deep Learning for Classification of Hyperspectral Data: A Comparative Review","date":"2019-04-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/shorten-spatial-spectral-rnn-with-parallel","title":"Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification","date":"2018-10-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/wide-contextual-residual-network-with-active","title":"Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification","date":"2018-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hyperspectral-image-classification-via-a","title":"Hyperspectral image classification via a random patches network","date":"2018-05-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hyperspectral-image-classification-with-1","title":"Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network","date":"2017-05-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spectral-spatial-classification-of-2","title":"Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network","date":"2017-01-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bass-net-band-adaptive-spectral-spatial","title":"BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification","date":"2016-12-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-supervised-learning-for-hyperspectral","title":"Deep supervised learning for hyperspectral data classification through convolutional neural networks","date":"2015-07-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/feature-extraction-of-hyperspectral-images","title":"Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering","date":"2013-09-16","rows_on_this_dataset":1,"code_links":0,"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."}