{"id":13597198,"url":"https://github.com/mhaut/hyperspectral_deeplearning_review","last_synced_at":"2025-04-10T00:31:48.255Z","repository":{"id":65737939,"uuid":"207014355","full_name":"mhaut/hyperspectral_deeplearning_review","owner":"mhaut","description":"Code of paper \"Deep Learning Classifiers for Hyperspectral Imaging: A Review\"","archived":false,"fork":false,"pushed_at":"2020-03-09T07:35:11.000Z","size":297,"stargazers_count":186,"open_issues_count":1,"forks_count":56,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-11-06T21:44:04.635Z","etag":null,"topics":["classification","deeplearning","hyperspectral","hyperspectral-image-classification","review"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mhaut.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-09-07T19:10:35.000Z","updated_at":"2024-10-11T08:53:37.000Z","dependencies_parsed_at":"2023-02-07T11:25:11.006Z","dependency_job_id":null,"html_url":"https://github.com/mhaut/hyperspectral_deeplearning_review","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mhaut%2Fhyperspectral_deeplearning_review","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mhaut%2Fhyperspectral_deeplearning_review/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mhaut%2Fhyperspectral_deeplearning_review/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mhaut%2Fhyperspectral_deeplearning_review/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mhaut","download_url":"https://codeload.github.com/mhaut/hyperspectral_deeplearning_review/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248135344,"owners_count":21053662,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classification","deeplearning","hyperspectral","hyperspectral-image-classification","review"],"created_at":"2024-08-01T17:00:24.775Z","updated_at":"2025-04-10T00:31:47.848Z","avatar_url":"https://github.com/mhaut.png","language":"Python","funding_links":[],"categories":["Remote Sensing"],"sub_categories":["Supervised"],"readme":"## Deep Learning Classifiers for Hyperspectral Imaging: A Review\n![](https://img.shields.io/github/stars/mhaut/hyperspectral_deeplearning_review.svg) ![](https://img.shields.io/github/forks/mhaut/hyperspectral_deeplearning_review.svg) ![](https://img.shields.io/github/issues/mhaut/hyperspectral_deeplearning_review.svg)  \nThe Code for \"Deep Learning Classifiers for Hyperspectral Imaging: A Review\".  \n[https://www.sciencedirect.com/science/article/pii/S0924271619302187]\n```\nM. E. Paoletti, J. M. Haut, J. Plaza and A. Plaza.\nDeep Learning Classifiers for Hyperspectral Imaging: A Review\nInternational Society for Photogrammetry and Remote Sensing\nDOI: 10.1016/j.isprsjprs.2019.09.006\nvol. 158, pp. 279-317, December 2019.\n```\n\n![reviewHSI](https://github.com/mhaut/hyperspectral_deeplearning_review/blob/master/images/paviaclasf.png)\n\n### Example of use\n```\n# Without datasets\ngit clone https://github.com/mhaut/hyperspectral_deeplearning_review/\n\n# With datasets\ngit clone --recursive https://github.com/mhaut/hyperspectral_deeplearning_review/\ncd HSI-datasets\npython join_dsets.py\n```\n\n### Run code\nGo to algorithms folder and run\n```\n# Training from scratch\npython \u003calgorithm\u003e.py --dataset IP \n# Example:\npython svm.py --dataset IP --tr_percent 0.15\n\n# Fine-tuning (not recommended) \u003cDENSENET121, MOBILENET, RESNET50, VGG16, VGG19\u003e:\npython pretrained_cnn.py --dataset IP --arch \u003carchitecture\u003e\n# Example:\npython pretrained_cnn.py --dataset IP --arch VGG16\n\n# Transfer learning \u003cCNN1D, CNN2D, CNN2D40bands, CNN3D\u003e, two steps:\npython transfer_learning.py --dataset1 IP --dataset2 SV --arch \u003calgorithm\u003e --search_base_model\npython transfer_learning.py --dataset1 IP --dataset2 SV --tr_samples 2 --use_val --arch \u003calgorithm\u003e --use_transfer_learning\n# Example:\npython transfer_learning.py --dataset1 IP --dataset2 SV --arch CNN2D40bands --search_base_model\npython transfer_learning.py --dataset1 IP --dataset2 SV --tr_samples 2 --use_val --arch CNN2D40bands --use_transfer_learning\n```\n\n#### Other parameters\nDimensionality reduction **- - components** [number]\n```\npython \u003calgorithm\u003e.py --dataset IP --components 40\n```\nYou can change the proposed parameters  **- - set_parameters** [parameters]\n```\npython svm.py --dataset IP --set_parameters --C 2 --g 0.01\n```\nYou can use validation set  **- - use_val** by default is 10%, you can change it **- -use_val - -val_percent** [percent]\n```\npython cnn1d.py --dataset IP --use_val --val_percent 0.10\n```\nExample:\n```\npython cnn1d.py --dataset IP --components 40  --set_parameters --epochs 100 --batch_size 32--use_val --val_percent 0.10\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmhaut%2Fhyperspectral_deeplearning_review","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmhaut%2Fhyperspectral_deeplearning_review","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmhaut%2Fhyperspectral_deeplearning_review/lists"}