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https://github.com/xiuheng-wang/awesome-hyperspectral-image-unmixing

Resource collection for the paper "Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing: A summary of current methods" (SPM 2023).
https://github.com/xiuheng-wang/awesome-hyperspectral-image-unmixing

List: awesome-hyperspectral-image-unmixing

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Resource collection for the paper "Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing: A summary of current methods" (SPM 2023).

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README 

        
## Awesome resources on Hyperspectral Image Unmixing

A list of hyperspectral image unmixing resources collected by Xiuheng Wang ([email protected]) and Min Zhao ([email protected]). **For more details, please refer to our paper: Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing: A summary of current methods.** [[Paper](https://ieeexplore.ieee.org/document/10054209)]. If you find any important resources are not included, please feel free to contact us.



    @article{chen2022integration,

      title={Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing: A summary of current methods}, 

      author={Chen, Jie and Zhao, Min and Wang, Xiuheng and Richard, Cédric and Rahardja, Susanto},

      journal={IEEE Signal Processing Magazine},

      volume={40},

      number={2},

      pages={61--74},

      year={2023},

      publisher={IEEE}

    }



## Contents



- [Physics-based mixture model](#model)

  - [Survey](#survey)

  - [Examples](#examples)

- [Classic unmixing methods](#classic)

- [Integrating of physics-based models in DNN design](#network)

- [Prior information learning with data-driven approaches](#iterative)

- [Integrating loss learnt from data into physics-based inverse problems](#loss)

- [Databases](#data)

- [Toolbox](#toolbox)






## Physics-based mixture model






### Survey

1. **Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art.** GRSM 2017, *P. Ghamisi et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8113122)]

2. **Nonlinear unmixing of hyperspectral images: Models and algorithms.** SPM 2013, *N. Dobigeon et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6678284)] 

3. **A review of nonlinear hyperspectral unmixing methods.** JSTARS 2014, *R. Heylen et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6816071)] 

4. **Spectral variability in hyperspectral data unmixing: A comprehensive review.** GRSM 2021, *R. Borsoi et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9439249)] [[Code](https://github.com/ricardoborsoi/unmixing_spectral_variability)] :fire:






### Examples

1. **Nonlinear unmixing of hyperspectral images using a generalized bilinear model.** TGRS 2011, *A. Halimi et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5702384)]

2. **Theory of reflectance and emittance spectroscopy.** Cambridge university press 2012, *B. Hapke*. 

[[Paper](https://www.cambridge.org/core/books/theory-of-reflectance-and-emittance-spectroscopy/C266E1164D5E14DA18141F03D0E0EAB0)]






## Classic unmixing methods

1. **Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery.** TGRS 2001, *D. C. Heinz et al*. [[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=911111)]

2. **Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization.** TGRS 2007, *L Miao et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4106058)]

3. **Low-rank and sparse representation for hyperspectral image processing: A review.** GRSM 2021, *J Peng et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9451654)]

4. **Nonlinear estimation of material abundances in hyperspectral images with ℓ1-norm spatial regularization.** TGRS 2013, *J. Chen et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6531654)] [[Code](http://www.jie-chen.com/index.php?nfssp=Reproducible)] :fire:

5. **Kernel-based nonlinear spectral unmixing with dictionary pruning.** RS 2019, *Z. Li et al*.

[[Paper](https://www.mdpi.com/2072-4292/11/5/529/pdf?version=1551778872)]  :fire:

6. **Non-linear spectral unmixing by geodesic simplex volume maximization.** JSTSP 2010, *R. Heylen et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5605217)]

7. **Fully constrained least squares spectral unmixing by simplex projection.** TGRS 2011, *R. Heylen et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5887410)] [[Code](https://web.archive.org/web/20221207130159/https://sites.google.com/site/robheylenresearch/code)]

8. **A distance geometric framework for nonlinear hyperspectral unmixing.** JSTARS 2014, *R. Heylen et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6814294)] [[Code](https://web.archive.org/web/20221207130159/https://sites.google.com/site/robheylenresearch/code)]






## Integrating of physics-based models in DNN design.

1. **EndNet: Sparse autoencoder network for endmember extraction and hyperspectral unmixing.** TGRS 2018, *S. Ozkan et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8429926)] [[Result](https://github.com/savasozkan/endnet)]

2. **uDAS: An untied denoising autoencoder with sparsity for spectral unmixing.** TGRS 2018, *Y. Qu et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8476591)] [[Code](https://github.com/aicip/uDAS)]

3. **DAEN: Deep autoencoder networks for hyperspectral unmixing.** TGRS 2019, *Y. Su et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8628241)] 

4. **Hyperspectral unmixing using a neural network autoencoder.** IEEE Access 2018, *B. Palsson et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8322133)] [[Code](https://github.com/burknipalsson/hu_autoencoders/blob/main/Method_DAEU.ipynb)]

5. **Convolutional autoencoder for spectral–spatial hyperspectral unmixing.** TGRS 2020, *B. Palsson et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8900297)] [[Code](https://github.com/burknipalsson/hu_autoencoders/blob/main/Method_CNNAEU.ipynb)]

6. **Hyperspectral unmixing using deep convolutional autoencoders in a supervised scenario.** JSTARS 2020, *F. Khajehrayeni et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8984691)] 

7. **Nonlinear unmixing of hyperspectral data via deep autoencoder network.** GRSL 2019, *M. Wang et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8667664)] [[Code](https://github.com/zhaomin0101/NAE)] :fire:

8. **LSTM-DNN based autoencoder network for nonlinear hyperspectral image unmixing.** JSTSP 2021, *M. Zhao et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9326377)] :fire:

9. **Hyperspectral unmixing for additive nonlinear models with a 3-D-CNN autoencoder network.** TGRS 2022, *M. Zhao et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9503107)] [[Code](https://github.com/zhaomin0101/3DCNN-var)] :fire:

10. **Deep autoencoders with multitask learning for bilinear hyperspectral unmixing.** TGRS 2020, *Y. Su et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9290391)] 

11. **Unsupervised hyperspectral unmixing via nonlinear autoencoders.** TGRS 2021, *K. T. Shahid et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9432042)] [[Code](https://github.com/KaziTShahid/Nonlinear-Hyperspectral-Unmixing-Autoencoder)]

12. **TANet: An unsupervised two-stream autoencoder network for hyperspectral unmixing.** TGRS 2021, *Q. Jin et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9489359)] [[Code](https://github.com/meixiaoguang/TANet)]

13. **Probabilistic generative model for hyperspectral unmixing accounting for endmember variability.**  TGRS 2022, *S. Shi et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9583297)] [[Code](https://github.com/shuaikaishi/PGMSU)] :fire:

14. **Deep generative endmember modeling: An application to unsupervised spectral unmixing.** TCI 2019, *R. Borsoi et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8878112)] [[Code](https://github.com/ricardoborsoi/Unmixing_with_Deep_Generative_Models)]

15. **Deep half-siamese networks for hyperspectral unmixing.** GRSL 2020, *Z. Han et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9160879)] 

16. **Dual branch autoencoder network for spectral-spatial hyperspectral unmixing.** GRSL 2021, *Z. Hua et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9474909)] 

17. **Cycu-net: Cycle-consistency unmixing network by learning cascaded autoencoders.** TGRS 2021, *L. Gao et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9383423)] [[Code](https://github.com/hanzhu97702/IEEE_TGRS_CyCU-Net)]

18. **UnDIP: Hyperspectral unmixing using deep image prior.** TGRS 2021, *B. Rasti et al*. 

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9392110)] [[Code](https://github.com/BehnoodRasti/UnDIP)]

19. **SSCU-Net: Spatial–spectral collaborative unmixing network for hyperspectral images.** TGRS 2022, *L. Qi et al*. 

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9709843)]

20. **Multimodal hyperspectral unmixing: Insights from attention networks.** TGRS 2022, *Z. Han et al*. 

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9724217)] [[Code](https://github.com/hanzhu97702/IEEE_TGRS_MUNet)]






## Prior information learning with data-driven approaches

1. **A plug-and-play priors framework for hyperspectral unmixing.** TGRS 2021, *M. Zhao et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9325040)] [[Code](https://github.com/xiuheng-wang/Plug_and_Play_HSI_unmixing)] :fire:

2. **Hyperspectral unmixing via nonnegative matrix factorization with handcrafted and learned priors.** GRSL 2021, *M. Zhao et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9321154)] :fire:

3. **Hyperspectral nonlinear unmixing by using plug-and-play prior for abundance maps** RS 2020, *Z. Wang et al*.

[[Paper](https://www.mdpi.com/2072-4292/12/24/4117)] 

4. **An ADMM based network for hyperspectral unmixing tasks.** ICASSP 2021, *C. Zhou et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9414555)] 

5. **SNMF-Net: Learning a deep alternating neural network for hyperspectral unmixing.** TGRS 2021, *F. Xiong et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9444347)] [[Code](http://www.xiongfuli.com/cv/code/SNMF.zip)]






## Integrating loss learnt from data into physics-based inverse problems

1. **JMnet: Joint metric neural network for hyperspectral unmixing.** TGRS 2021, *A. Min et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9404359)] 

2. **Adversarial autoencoder network for hyperspectral unmixing.** TNNLS 2021, *Q. Jin et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9565143)] [[Code](https://github.com/meixiaoguang/AAENet)]






## Databases



1. **A laboratory-created dataset with ground truth for hyperspectral unmixing evaluation.** JSTARS 2019, *M. Zhao et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8681610)] [[Data](http://www.jie-chen.com/index.php?nfssp=Data)] :fire:






## Toolbox



1. **Blind hyperspectral unmixing using autoencoders: A critical comparison.** JSTARS 2022, *B. Palsson et al*.

[[Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9672688)] [[Code](https://github.com/burknipalsson/hu_autoencoders)]