{"id":13498970,"url":"https://github.com/suvojit-0x55aa/A2S2K-ResNet","last_synced_at":"2025-03-29T03:32:14.042Z","repository":{"id":48628554,"uuid":"268297578","full_name":"suvojit-0x55aa/A2S2K-ResNet","owner":"suvojit-0x55aa","description":"A2S2K-ResNet: Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification","archived":false,"fork":false,"pushed_at":"2022-11-25T15:25:36.000Z","size":5515,"stargazers_count":189,"open_issues_count":2,"forks_count":37,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-10-31T17:38:54.618Z","etag":null,"topics":["3d-cnn","cnn","colab","colab-notebook","colaboratory","hyperspectral","hyperspectral-image-classification","hyperspectral-imaging","image-classification","remote-sensing","residual-networks"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/suvojit-0x55aa.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-05-31T14:31:21.000Z","updated_at":"2024-10-17T15:10:36.000Z","dependencies_parsed_at":"2023-01-23T09:31:11.058Z","dependency_job_id":null,"html_url":"https://github.com/suvojit-0x55aa/A2S2K-ResNet","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/suvojit-0x55aa%2FA2S2K-ResNet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/suvojit-0x55aa%2FA2S2K-ResNet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/suvojit-0x55aa%2FA2S2K-ResNet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/suvojit-0x55aa%2FA2S2K-ResNet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/suvojit-0x55aa","download_url":"https://codeload.github.com/suvojit-0x55aa/A2S2K-ResNet/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246135713,"owners_count":20729056,"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":["3d-cnn","cnn","colab","colab-notebook","colaboratory","hyperspectral","hyperspectral-image-classification","hyperspectral-imaging","image-classification","remote-sensing","residual-networks"],"created_at":"2024-07-31T22:00:23.718Z","updated_at":"2025-03-29T03:32:13.263Z","avatar_url":"https://github.com/suvojit-0x55aa.png","language":"Python","funding_links":[],"categories":["3 Code"],"sub_categories":["3.1 Comparison methods of our proposed EMS-GCN methods"],"readme":"[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/attention-based-adaptive-spectral-spatial/hyperspectral-image-classification-on-kennedy)](https://paperswithcode.com/sota/hyperspectral-image-classification-on-kennedy?p=attention-based-adaptive-spectral-spatial)\n\t\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/attention-based-adaptive-spectral-spatial/hyperspectral-image-classification-on-pavia)](https://paperswithcode.com/sota/hyperspectral-image-classification-on-pavia?p=attention-based-adaptive-spectral-spatial)\n\t\n[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/attention-based-adaptive-spectral-spatial/hyperspectral-image-classification-on-indian)](https://paperswithcode.com/sota/hyperspectral-image-classification-on-indian?p=attention-based-adaptive-spectral-spatial)\n\n\n# Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification\n\nThis repository is the official implementation of [Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification](https://ieeexplore.ieee.org/document/9306920). \n[![Open A2S2K-ResNet in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1x2CYfaUXNjX4yDMLCvoVFqAMZXZwwVgS)\n\n\n\u003e📋  Abstract:\nHyperspectral images (HSIs) provide rich spectral-spatial information with stacked hundreds of contiguous narrowbands. Due to the existence of noise and band correlation, the selection of informative spectral-spatial kernel features poses a challenge. This is often addressed by using convolutional neural networks (CNNs) with receptive field (RF) having fixed sizes. However, these solutions cannot enable neurons to effectively adjust RF sizes and cross-channel dependencies when forward and backward propagations are used to optimize the network. In this article, we present an attention-based adaptive spectral-spatial kernel improved residual network (A²S²K-ResNet) with spectral attention to capture discriminative spectral-spatial features for HSI classification in an end-to-end training fashion. In particular, the proposed network learns selective 3-D convolutional kernels to jointly extract spectral-spatial features using improved 3-D ResBlocks and adopts an efficient feature recalibration (EFR) mechanism to boost the classification performance. Extensive experiments are performed on three well-known hyperspectral data sets, i.e., IP, KSC, and UP, and the proposed A²S²K-ResNet can provide better classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa compared with the existing methods investigated.\n\n\n\u003cimg src=\"figs/model.png\"/\u003e\n\n## Requirements\n\nTo install requirements:\n\n```setup\nconda env create -f environment.yml\n```\n\nTo download the dataset and setup the folders, run:\n\n```\nbash setup_script.sh\n```\n\n## Training\n\nTo train the model(s) in the paper, run this command in the A2S2KResNet folder:\n\n```train\npython A2S2KResNet.py -d \u003cIN|UP|KSC\u003e -e 200 -i 3 -p 3 -vs 0.9 -o adam\n```\n\n## Results\n\nOur model achieves the following performance on 10% of datasets:\n\n### [India Pines](http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat) dataset\n\n| Model name         | OA  |\n| ------------------ |---------------- |\n| A2S2K-ResNet   | 98.66 ± 0.004 % |\n\n### [Kennedy Space Center](http://www.ehu.es/ccwintco/uploads/2/26/KSC.mat) dataset\n\n| Model name         | OA  |\n| ------------------ |---------------- |\n| A2S2K-ResNet   | 99.34 ± 0.001 % |\n\n### [University of Pavia](http://www.ehu.eus/ccwintco/uploads/e/ee/PaviaU.mat) dataset\n\n| Model name         | OA  |\n| ------------------ |---------------- |\n| A2S2K-ResNet   | 99.85 ± 0.001 % |\n\nFor deatiled results refer to Table IV-VII of our paper. \n\n\n## Citation\n\nIf you use A2S2K-ResNet code in your research, we would appreciate a citation to the original paper:\n```\n@article{roy2020attention,\n\ttitle={Attention-based adaptive spectral-spatial kernel resnet for hyperspectral image classification},\n\tauthor={Swalpa Kumar Roy, and Suvojit Manna, and Tiecheng Song, and Lorenzo Bruzzone},\n\tjournal={IEEE Transactions on Geoscience and Remote Sensing},\n\tvolume={59},\n\tno.={9},\n\tpp.={7831-7843},\n\tyear={2021},\n\tpublisher={IEEE}\n\t}\t\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsuvojit-0x55aa%2FA2S2K-ResNet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsuvojit-0x55aa%2FA2S2K-ResNet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsuvojit-0x55aa%2FA2S2K-ResNet/lists"}