{"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/jigsawhsi-a-network-for-hyperspectral-image","title":"JigsawHSI: a network for Hyperspectral Image classification","arxiv_id":"2206.02327","date":"2022-06-06","proceeding":null,"authors":["Jaime Moraga"],"abstract":"This article describes Jigsaw, a convolutional neural network (CNN) used in geosciences and based on Inception but tailored for geoscientific analyses. Introduces JigsawHSI (based on Jigsaw) and uses it on the land-use land-cover (LULC) classification problem with the Indian Pines, Pavia University and Salinas hyperspectral image data sets. The network is compared against HybridSN, a spectral-spatial 3D-CNN followed by 2D-CNN that achieves state-of-the-art results on the datasets. This short article proves that JigsawHSI is able to meet or exceed HybridSN's performance in all three cases. It also introduces a generalized Jigsaw architecture in d-dimensional space for any number of multimodal inputs. Additionally, the use of jigsaw in geosciences is highlighted, while the code and toolkit are made available.","url_abs":"https://arxiv.org/abs/2206.02327v3","url_pdf":"https://arxiv.org/pdf/2206.02327v3.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":"jigsawhsi-a-network-for-hyperspectral-image","repo_url":"https://github.com/jmoraga-mines/jigsawhsi","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"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":[{"method_slug":"jigsaw","method_name":"Jigsaw"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hyperspectral-image-classification-on-indian","task":"Hyperspectral Image Classification","dataset":"Indian Pines","model":"JigsawHSI","rank_in_archive_order":23,"of":34,"metrics":{"Overall Accuracy":"99.74"},"uses_additional_data":true},{"leaderboard":"/sota/hyperspectral-image-classification-on-pavia","task":"Hyperspectral Image Classification","dataset":"Pavia University","model":"JigsawHSI","rank_in_archive_order":13,"of":33,"metrics":{"Overall Accuracy":"100.00"},"uses_additional_data":false},{"leaderboard":"/sota/hyperspectral-image-classification-on-salinas-1","task":"Hyperspectral Image Classification","dataset":"Salinas","model":"JigsawHSI","rank_in_archive_order":1,"of":3,"metrics":{"OA@200":"100.00"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}