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https://github.com/IRCAD/awesome-hyperspectral-deep-learning

A curated list of papers and ressources linked to Deep Learning analysis of Hyperspectral Images
https://github.com/IRCAD/awesome-hyperspectral-deep-learning

List: awesome-hyperspectral-deep-learning

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A curated list of papers and ressources linked to Deep Learning analysis of Hyperspectral Images

Host: GitHub
URL: https://github.com/IRCAD/awesome-hyperspectral-deep-learning
Owner: IRCAD
Created: 2020-07-20T10:09:14.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2021-12-07T07:03:41.000Z (almost 3 years ago)
Last Synced: 2024-05-22T23:01:57.057Z (5 months ago)
Size: 23.4 KB
Stars: 28
Watchers: 7
Forks: 3
Open Issues: 1
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- Code of conduct: code-of-conduct.md

Awesome Lists containing this project

awesome-hyperspectral - awesome-hyperspectral-deep-learning
ultimate-awesome - awesome-hyperspectral-deep-learning - A curated list of papers and ressources linked to Deep Learning analysis of Hyperspectral Images. (Other Lists / PowerShell Lists)

README

        # Awesome Hyperspectral Deep Learning[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

> A curated list of papers and ressources linked to Deep Learning analysis of Hyperspectral Images

## Contents

- [Remote Sensing](#remote-sensing)

    - [Supervised](#supervised)

    - [Unsupervised](#unsupervised)

- [Medical](#medical)

- [Multi-Spectral Surgical Imaging](#multi-spectral-surgical-imaging)

- [Misc](#misc)

- [Bibliography Tool](#bibliography-tool)

- [General](#general)

    - [Data Augmentation](#data-augmentation)

    - [Loss Functions](#loss-functions)

    - [Normalization](#normalization)

    - [Anomaly Detection](#anomaly-detection)

    - [HSI distance metrics](#hsi-distance-metrics)

## Remote Sensing

### Supervised

- **Deep learning classifiers for hyperspectral imaging: A review** (2020), Paoletti et al. [[html]](https://github.com/mhaut/hyperspectral_deeplearning_review)

- **Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field** (2019), Liang et al. [[pdf]](https://arxiv.org/pdf/1903.06258)

- **Deep Learning for Hyperspectral Image Classification on Embedded Platforms** (2018), Balakrishnan et al. [[html]](https://ieeexplore.ieee.org/document/8708899)

- **Hyperspectral Image Classification With Convolutional Neural Network and Active Learning** (2020), Cao et al. [[html]](https://ieeexplore.ieee.org/document/8978543)

- **Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review** (2019), Signoroni et al. [[pdf]](https://www.mdpi.com/2313-433X/5/5/52/pdf)

- **Learning Hyperspectral Feature Extraction and Classification with ResNeXtNetwork** (2020), Nyasaka et al. [[pdf]](https://arxiv.org/pdf/2002.02585.pdf)

- **HybridSN: Exploring 3D-2D CNN FeatureHierarchy for Hyperspectral Image Classification** (2019), Roy et al. [[pdf]](https://arxiv.org/pdf/1902.06701.pdf)

- **Spectral–spatial residual network for hyperspectral image classification: A 3-D deep learning framework** (2017), Zhong et al. [[pdf]](https://www.researchgate.net/profile/Zilong_Zhong2/publication/320145277_Spectral-Spatial_Residual_Network_for_Hyperspectral_Image_Classification_A_3-D_Deep_Learning_Framework/links/5ce39604458515712eb894b0/Spectral-Spatial-Residual-Network-for-Hyperspectral-Image-Classification-A-3-D-Deep-Learning-Framework.pdf)

- **Deep Convolutional Capsule Network for Hyperspectral Image Spectral and Spectral-Spatial Classification** (2019), Zhu et al. [[pdf]](https://www.mdpi.com/2072-4292/11/3/223/pdf)

- **Learning Deep Hierarchical Spatial–Spectral Features for Hyperspectral Image Classification Based on Residual 3D-2D CNN** (2019), Feng et al. [[html]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928880/)

- **Deep Convolutional Neural Networks for Hyperspectral Image Classification** (2015), Hu et al. [[pdf]](https://pdfs.semanticscholar.org/0089/95a880625650509422ec7dfb6f4afdc43086.pdf?_ga=2.250225103.816551337.1581496739-1979552906.1581496739)

- **DEEP LEARNING APPROACH FOR REMOTE SENSING IMAGE ANALYSIS** (2016), Ben Hamida et al. [[html]](https://hal.archives-ouvertes.fr/hal-01370161/)

- **Deep Learning for Classification of Hyperspectral Data: A Comparative Review** (2019), Audebert et al. [[pdf]](https://arxiv.org/pdf/1904.10674.pdf)

- **Deep Recurrent Neural Networks for Hyperspectral Image Classification** (2017), Mou et al. [[pdf]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7914752)

- **Deep supervised learning for hyperspectral data classification through convolutional neural networks** (2015), Makantasis et al. [[pdf]](http://users.ntua.gr/karank/img/Makantasis_etal_igrass15.pdf)

- **Going Deeper with Contextual CNN for Hyperspectral Image Classification** (2017), Lee et Kwon [[pdf]](https://arxiv.org/pdf/1604.03519.pdf)

- **New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning** (2017), Ghamisi et al. [[pdf]](https://hal.archives-ouvertes.fr/hal-01854061/document)

- **Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network** (2017), Li et al. [[pdf]](https://www.mdpi.com/2072-4292/9/1/67/pdf)

### Unsupervised

- **Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging** (2016), Zabalza et al. [[html]](https://www.sciencedirect.com/science/article/pii/S0925231215017798)

- **Semi-supervised classification of hyperspectral imagery based on stacked autoencoders** (2016), Fu et al. [[html]](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10033/100332B/Semi-supervised-classification-of-hyperspectral-imagery-based-on-stacked-autoencoders/10.1117/12.2245011.short?SSO=1)

- **Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network** (2015), Chen et al. [[pdf]](https://oss.labxing.com/files/lab_publications/615-1533736397-YQcWOSQs.pdf)

- **Unsupervised Spectral–Spatial Feature Learning via Deep Residual Conv–Deconv Network for Hyperspectral Image Classification** (2018), Mou et al. [[pdf]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8082108)

## Medical

- **Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations** (2018), Fabelo et al. [[html]](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193721)

- **In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer** (2019), Halicek et al. [[html]](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6627361/)

- **Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries** (2017), Noor et al. [[pdf]](https://www.mdpi.com/1424-8220/17/11/2644/pdf)

- **Tumor semantic segmentation in hyperspectral images using deep learning** (2019), Trajanovski et al. [[pdf]](Tumor semantic segmentation in hyperspectral images using deep learning)

- **Medical hyperspectral imaging: a review** (2014), Lu et Fei [[pdf]](https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics/volume-19/issue-1/010901/Medical-hyperspectral-imaging-a-review/10.1117/1.JBO.19.1.010901.pdf)

- **A Dual Stream Network for Tumor Detection in Hyperspectral Images** (2019), Weijtmans et al. [[pdf]](https://www.researchgate.net/profile/Caifeng_Shan/publication/334407567_A_Dual_Stream_Network_for_Tumor_Detection_in_Hyperspectral_Images/links/5d9b3449a6fdccfd0e7fb9eb/A-Dual-Stream-Network-for-Tumor-Detection-in-Hyperspectral-Images.pdf)

- **Cell classification using convolutional neural networks in medical hyperspectral imagery** (2017), Xiang Li et al. [[html]](https://ieeexplore.ieee.org/abstract/document/7984606)

- **Convolutional neural network for medical hyperspectral image classification with kernel fusion** (2018), Huang et al. [[html]](https://ieeexplore.ieee.org/abstract/document/8470659)

- **Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging** (2017), Halicek et al. [[pdf]](https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics/volume-22/issue-6/060503/Deep-convolutional-neural-networks-for-classifying-head-and-neck-cancer/10.1117/1.JBO.22.6.060503.pdf)

- **Medical Hyperspectral Image Classification Based on End-to-End Fusion Deep Neural Network** (2017), Wei et al. [[html]](https://ieeexplore.ieee.org/abstract/document/8611167)

- **Hyperspectral Tissue Image Segmentation usingSemi-Supervised NMF and Hierarchical Clustering** (2018), Kumar et al. [[html]](https://ieeexplore.ieee.org/abstract/document/8543868)

## Multi-Spectral Surgical Imaging

- **Surgical spectral imaging** (2020), Clancy et al. [[html]](https://www.sciencedirect.com/science/article/pii/S1361841520300645)

## Misc

- **HYPerspectral Enhanced Reality (HYPER): a physiology-based surgical guidance tool** (2019), Barberio et al. [[html]](https://link.springer.com/article/10.1007/s00464-019-06959-9)

- **Hyperspectral enhanced reality (HYPER) for anatomical liver resection** (2020), Urade et al. [[html]](https://link.springer.com/article/10.1007%2Fs00464-020-07586-5)

- **Indocyanine-green-loaded microballoons for biliary imaging in cholecystectomy** (2012), Mitra et al. [[html]](https://www.spiedigitallibrary.org/journals/Journal-of-Biomedical-Optics/volume-17/issue-11/116025/Indocyanine-green-loaded-microballoons-for-biliary-imaging-in-cholecystectomy/10.1117/1.JBO.17.11.116025.full)

## Bibliography tool

- **Announcing Connected Papers — a visual tool for researchers to find and explore academic papers** [[html]](https://medium.com/connectedpapers/announcing-connected-papers-a-visual-tool-for-researchers-to-find-and-explore-academic-papers-89146a54c7d4)

- **Connected Papers** [[html]](http://connectedpapers.com/)

## General

### Data Augmentation

- **Hyperspectral Data Augmentation** (2019), Nalepa et al. [[pdf]](https://arxiv.org/pdf/1903.05580)

- **On data augmentation for segmenting hyperspectral images** (2019), Nalepa et al. [[pdf]](https://spie.org/Publications/Proceedings/Paper/10.1117/12.2519517?SSO=1)

### Loss functions

- **Loss Functions for Medical Image Segmentation: A Taxonomy**[[html]](https://medium.com/@junma11/loss-functions-for-medical-image-segmentation-a-taxonomy-cefa5292eec0)

### Normalization

- **Does normalization methods play a role for hyperspectral image classification?** (2017), Cao et al. [[pdf]](https://arxiv.org/pdf/1710.02939;Does)

- **Advanced Preprocessing: Sample Normalization** [[html]](http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Sample_Normalization)

### Anomaly detection

- **Discriminative Reconstruction Constrained Generative Adversarial Network for Hyperspectral Anomaly Detection** (2020), Jiang et al. [[html]](https://ieeexplore.ieee.org/abstract/document/8972475)

### HSI distance metrics

- **A Comprehensive Evaluation of Spectral Distance Functions and Metrics for Hyperspectral Image Processing** (2015), Deborah et al. [[pdf]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7061924)

## Contribute

Contributions welcome! Read the [contribution guidelines](contributing.md) first.

## License

[![CC0](https://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0)

To the extent possible under law, Gaël Mukunde has waived all copyright and

related or neighboring rights to this work.