{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bass-net-band-adaptive-spectral-spatial","title":"BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification","arxiv_id":"1612.00144","date":"2016-12-01","proceeding":null,"authors":["Anirban Santara","Kaustubh Mani","Pranoot Hatwar","Ankit Singh","Ankur Garg","Kirti Padia","Pabitra Mitra"],"abstract":"Deep learning based landcover classification algorithms have recently been\nproposed in literature. In hyperspectral images (HSI) they face the challenges\nof large dimensionality, spatial variability of spectral signatures and\nscarcity of labeled data. In this article we propose an end-to-end deep\nlearning architecture that extracts band specific spectral-spatial features and\nperforms landcover classification. The architecture has fewer independent\nconnection weights and thus requires lesser number of training data. The method\nis found to outperform the highest reported accuracies on popular hyperspectral\nimage data sets.","url_abs":"http://arxiv.org/abs/1612.00144v2","url_pdf":"http://arxiv.org/pdf/1612.00144v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bass-net-band-adaptive-spectral-spatial","repo_url":"https://github.com/kaustubh0mani/BASS-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"hyperspectral-image-classification","task_name":"Hyperspectral Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hyperspectral-image-classification-on-indian","task":"Hyperspectral Image Classification","dataset":"Indian Pines","model":"BASSNet","rank_in_archive_order":31,"of":34,"metrics":{"Overall Accuracy":"96.77%"},"uses_additional_data":false},{"leaderboard":"/sota/hyperspectral-image-classification-on-pavia","task":"Hyperspectral Image Classification","dataset":"Pavia University","model":"BASSNet","rank_in_archive_order":31,"of":33,"metrics":{"Overall Accuracy":"97.48%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1612.00144","atlas_url":"https://app.syntology.ai/?focus=1612.00144","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}