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https://github.com/immortal13/awesome-hyperspectral-image-classification

An comprehensive list of hyperspectral image classification resources (papers & codes & related websites) collected by Jiaqi Zou ([email protected])
https://github.com/immortal13/awesome-hyperspectral-image-classification

List: awesome-hyperspectral-image-classification

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An comprehensive list of hyperspectral image classification resources (papers & codes & related websites) collected by Jiaqi Zou ([email protected])

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# Awesome-hyperspectral-image-classification

An comprehensive list of hyperspectral image classification resources (papers & codes & related websites) collected by Jiaqi Zou ([email protected])



Nice meeting you, please feel free to share this list with others!



Contributions in any form to make this list more comprehensive are welcome!



If you find this repository useful, kindly consider citing or giving it a star★ (*^▽^*).



## Overview

- [Awesome-hyperspectral-image-classification](#awesome-hyperspectral-image-classification)

  * [1 Survey paper](#1-survey-paper)

  * [2 Advanced paper](#2-advanced-paper)

  * [3 Code](#3-code)

    + [3.1 Comparison methods of our proposed EMS-GCN methods](#31-comparison-methods-of-our-proposed-ems-gcn-methods)

    + [3.2 Comparison methods of our proposed LESSFormer methods](#32-comparison-methods-of-our-proposed-lessformer-methods)

    + [3.3 Other open source codes](#33-other-open-source-codes)

      - [3.3.1 Traditional algorithm](#331-traditional-algorithm)

      - [3.3.2 Deep learning algorithm](#332-deep-learning-algorithm)

  * [4 Dataset](#4-dataset)



## 1 Survey paper

Discriminant analysis-based dimension reduction for hyperspectral image classification: A survey of the most recent advances and an experimental comparison of different techniques. IEEE TGRS, 2018. [paper](https://ieeexplore.ieee.org/abstract/document/8329024/)



Deep learning for hyperspectral image classification: An overview. IEEE TGRS, 2019. [paper](https://ieeexplore.ieee.org/abstract/document/8697135)



Deep learning classifiers for hyperspectral imaging: A review. ISPRS, 2019. [paper](https://www.sciencedirect.com/science/article/pii/S0924271619302187)



Deep learning for classification of hyperspectral data: A comparative review. IEEE GRSM, 2019. [paper](https://ieeexplore.ieee.org/abstract/document/8738045)



An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges. Information fusion, 2020. [paper](https://www.sciencedirect.com/science/article/pii/S1566253519307857)



Feature extraction for hyperspectral imagery: The evolution from shallow to deep: Overview and toolbox. IEEE GRSM, 2020. [paper](https://ieeexplore.ieee.org/abstract/document/9082155/)



Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: A review. JAG, 2021. [paper](https://www.sciencedirect.com/science/article/pii/S030324342100310X)



A survey: Deep learning for hyperspectral image classification with few labeled samples. Neurocomputing, 2021. [paper](https://www.sciencedirect.com/science/article/pii/S0925231221004033)



Multi-view learning for hyperspectral image classification: An overview. Neurocomputing, 2022. [paper](https://www.sciencedirect.com/science/article/pii/S0925231222006762)



Active learning for hyperspectral image classification: A comparative review. IEEE GRSM, 2022. [paper](https://ieeexplore.ieee.org/abstract/document/9774342/)



Land Use and Land Cover Classification with Hyperspectral Data: A comprehensive review of methods, challenges and future directions. Neurocomputing, 2023. [paper](https://www.sciencedirect.com/science/article/pii/S0925231223002436#s0120)



**--- Specific areas and other ---**



First hyperspectral imaging survey of the deep seafloor: High-resolution mapping of manganese nodules. RSE, 2018. [paper](https://www.sciencedirect.com/science/article/pii/S0034425718300300)



Three-dimensional convolutional neural network model for tree species classification using airborne hyperspectral images. RSE 2020. [paper](https://www.sciencedirect.com/science/article/pii/S0034425720303084)



A survey of landmine detection using hyperspectral imaging. ISPRS, 2017. [paper](https://www.sciencedirect.com/science/article/pii/S0924271616306451)



Early decay detection in fruit by hyperspectral imaging–Principles and application potential. Food Control, 2023. [paper](https://www.sciencedirect.com/science/article/pii/S095671352300230X)



Low-rank and sparse representation for hyperspectral image processing: A review. IEEE GRSM, 2021. [paper](https://ieeexplore.ieee.org/abstract/document/9451654)



A survey on superpixel segmentation as a preprocessing step in hyperspectral image analysis. IEEE JSTARS, 2021. [paper](https://ieeexplore.ieee.org/abstract/document/9416734/)



## 2 Advanced paper

Methanemapper: Spectral absorption aware hyperspectral transformer for methane detection. CVPR 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Kumar_MethaneMapper_Spectral_Absorption_Aware_Hyperspectral_Transformer_for_Methane_Detection_CVPR_2023_paper.html)



Quantum-Inspired Spectral-Spatial Pyramid Network for Hyperspectral Image Classification. CVPR 2023. [paper](https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Quantum-Inspired_Spectral-Spatial_Pyramid_Network_for_Hyperspectral_Image_Classification_CVPR_2023_paper.html)



(to be completed 😋)



## 3 Code

### 3.1 Comparison methods of our proposed EMS-GCN methods



[DeepHyperX](https://github.com/nshaud/DeepHyperX)——A Python tool to perform deep learning experiments on various hyperspectral datasets.



[ExtendedMorphologicalProfiles](https://github.com/andreybicalho/ExtendedMorphologicalProfiles)——Use the Extended Morphological Profiles and Support Vector Machines to classify remote sensed hyperspectral images using Python.



[SGL](https://github.com/psellcam/Superpixel-Contracted-Graph-Based-Learning-for-Hyperspectral-Image-Classification)——Code for the Paper "Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification"



[HybridSN](https://github.com/gokriznastic/HybridSN)——A keras based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".



[pResNet-HSI](https://github.com/mhaut/pResNet-HSI)——Source code of "Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification"



[A2S2K-ResNet](https://github.com/suvojit-0x55aa/A2S2K-ResNet)——Official implementation of Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification.



[MDGCN](https://github.com/LEAP-WS/MDGCN)——Repository for the TGRS paper [Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification].



[CEGCN](https://github.com/qichaoliu/CNN_Enhanced_GCN)——Repository for the TGRS paper CNN-Enhanced Graph Convolutional Network with Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification



### 3.2 Comparison methods of our proposed LESSFormer methods



[HPDM-SPRN](https://github.com/shangsw/HPDM-SPRN)——Repository for the paper: Spectral Partitioning Residual Network with Spatial Attention Mechanism for Hyperspectral Image Classification.



[FreeNet](https://github.com/Z-Zheng/FreeNet)——Official implementation for TGRS 2020 paper "FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification".



[HiT](https://github.com/xiachangxue/DeepHyperX)——Add some transformer-based HSI classification models.



[SSFTT](https://github.com/zgr6010/HSI_SSFTT)——Repository for the paper: Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification.



[SpectralFormer](https://github.com/danfenghong/IEEE_TGRS_SpectralFormer)——Repository for the paper: Spectralformer: Rethinking hyperspectral image classification with transformers. 



### 3.3 Other open source codes

#### 3.3.1 Traditional algorithm

[SVM](https://github.com/immortal13/SVM-hyperspectral-image-classification)



(to be completed 😋)

#### 3.3.2 Deep learning algorithm

[EMS-GCN](https://github.com/immortal13/EMS-GCN-hyperspectral-image-classification)



(to be completed 😋)



## 4 Dataset

[Download URL of some public available hyperspectral scenes](https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes#Pavia_University_scene)



[Introduction of public available hyperspectral scenes](https://mp.weixin.qq.com/s?__biz=MzI1OTQyMzYyMg==&mid=2247483715&idx=1&sn=5da6a9033e5444980c379944bc939ff6&chksm=ea786daadd0fe4bcd6c57082c7268505d867d717558984ab59413b7e9070802255898fb51410&mpshare=1&scene=23&srcid=&sharer_sharetime=1583118496004&sharer_shareid=2a63c2bce533109f6ee92fb7712f7400#rd)