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🌇🌆 Benchmarking of Hyperspectral Image Fusion methods 🏙🌃

🌇🌆 Hyperspectral Image Fusion Benchmarking 🏙🌃

Comparison of the multispectral (MS) and hyperspectral (HS) image fusion techniques used for the spatial resolution enhancement of HS images.

diagram

Existing hyperspectral imaging systems produce images that lack spatial resolution due to hardware limitations. Even with the proven efficacy of this technology in several computer vision tasks, the aforementioned limitation obstructs its applicability. Contrarily, conventional RGB images have a much larger resolution with just three spectra. Since the issue of low resolution images arises from hardware limitations, there have been several developments in software-based approaches to improve the spatial resolution of hyperspectral images.

This work allows for an easy-to-use framework for testing and comparing existing hyperspectral image fusion (HIF) methods for spatial resolution enhancement.

Content

  • Citation
  • Instructions
  • Datasets
  • Methods
    • Implemented Methods
    • Other Methods
    • Extensions
  • Metrics
  • Requirements

Citation

If you use any part of this work, please use the following citation:

Magalhães, Miguel. “Hyperspectral Image Fusion: A Comprehensive Review”. Master’s Programme in Imaging and Light in Extended Reality (IMLEX). MSc. thesis. KU Leuven, 2022.

@mastersthesis{hif_review_2022,
    title={Hyperspectral Image Fusion: A Comprehensive Review},
    author={Miguel Magalhães},
    year={2022},
    school={KU Leuven},
    note={Master’s Programme in Imaging and Light in Extended Reality (IMLEX)}
}

Instructions

Download and process dataset(s) (e.g.: CAVE, Harvard). This will also create MS image and downsampled HS image by a factor of 4, 8 and 16 (or any other power of 2 that you add as input to the script):

python main/dataset_CAVE.py

Run all algorithms over the datasets (you can edit run.py to customize the combinatory that you wish to process in terms of datasets, methods and scaling factors):

python main/run.py

Finally, compute the metrics that compare the output of the image fusion methods with the ground truth data:

python main/metrics.py

Datasets

Compilation of publically available hyperspectral datasets. The datasets in bold can be automatically downloaded and processed using the respective script main/dataset_{name}.py as per the instructions above.

Dataset Year Qty Resolution* Download Paper
CAVE 2008 32 512x512x31 [400,700]nm All Yasuma, F., Mitsunaga, T., Iso, D., & Nayar, S. K. (2010). Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum. IEEE transactions on image processing, 19(9), 2241-2253.
Harvard 2011 77 1040x1392x31 [420,720]nm All Chakrabarti, A., & Zickler, T. (2011, June). Statistics of real-world hyperspectral images. In CVPR 2011 (pp. 193-200). IEEE.
NUS** 2014 88 ?×?x31 [400,700]nm - Nguyen, R. M., Prasad, D. K., & Brown, M. S. (2014, September). Training-based spectral reconstruction from a single RGB image. In European Conference on Computer Vision (pp. 186-201). Springer, Cham.
iCVL** 2016 201 1392×1300x519 [400,1000]nm All Arad, B., & Ben-Shahar, O. (2016, October). Sparse recovery of hyperspectral signal from natural RGB images. In European Conference on Computer Vision (pp. 19-34). Springer, Cham.

As a demo image, we include the hyperspectral measurement of a resolution chart (ISO 12233:2017 Edge eSFR Inkjet chart) with a resolution of 512x512x108 and a wavelength interval from 403.09nm to 717.54nm, measured with a Specim IQ camera.

resolution-chart

Additionally, remote sensing hyperspectral scenes are also available and widely used accross the field.

Click to show list of Hyperspectral Remote Sensing Scenes

Below, we list the publically available hyperspectral remote sensing scenes. The ones in italic were collected by the GIC from EHU, and can be downloaded using main/_dataset_EHU.py, the processing part to generate the MS and downsampled HS images is still missing.

Dataset Year Qty Resolution* Download Paper
Indian Pines 1992 1 145x145x220 [400,2500]nm*** URL Baumgardner, M. F., Biehl, L. L., & Landgrebe, D. A. (2015). 220 band aviris hyperspectral image data set: June 12, 1992 indian pine test site 3. Purdue University Research Repository, 10, R7RX991C. / AVIRIS NASA.
Kennedy Space Center 1996 1 512x614x176 [400,2500]nm*** URL AVIRIS NASA. Information about removed bands unavailable.
Salinas 1998 1 512x217x224 [400,2500]nm*** Full Subscene AVIRIS NASA.
Cuprite 1998 1 512x614x224 [400,2500]nm*** URL AVIRIS NASA.
Botswana 2001 1 1476x256x145 [400,2500]nm*** URL AVIRIS NASA. Information about removed bands is incorrect.
Pavia 2008 2 ?x?x103 [430,860]nm Centre University Dataset provided by Prof. Paolo Gamba from the Telecommunications and Remote Sensing Laboratory, Pavia university (Italy).
Chikusei** 2016 1 2517x2335x128 [363,1018]nm URL Yokoya, N., & Iwasaki, A. (2016). Airborne hyperspectral data over Chikusei. Space Appl. Lab., Univ. Tokyo, Tokyo, Japan, Tech. Rep. SAL-2016-05-27.
WHU-Hi** 2020 3 varies All Zhong, Y., Hu, X., Luo, C., Wang, X., Zhao, J., & Zhang, L. (2020). WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF. Remote Sensing of Environment, 250, 112012.

Further remote sensing scenes can be found at rslab.ut.ac.ir/data.

* the first line represents the size of the spectral cubes (width x height x spectral bands), and the second line the wavelength interval of the dataset.

** script for automatic download and processing not implemented yet.

*** some bands in between were removed.

Methods

Hyperspectral image fusion (HIF) methods with code publicly available.

Implemented Methods

Methods with code available together with an implemented wrapper in this repository (some of the wrappers are adapted from "Hyperspectral and Multispectral Data Fusion: A Comparative Review" [^1]).

Method Year Code Paper
SFIM* 2000 Matlab Liu, J. G. (2000). Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21(18), 3461-3472.
MAPSMM 2004 Matlab Eismann, M. T. (2004). Resolution enhancement of hyperspectral imagery using maximum a posteriori estimation with a stochastic mixing model. University of Dayton.
GLP* 2006 Matlab Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., & Selva, M. (2006). MTF-tailored multiscale fusion of high-resolution MS and Pan imagery. Photogrammetric Engineering & Remote Sensing, 72(5), 591-596.
GSA 2007 Matlab Aiazzi, B., Baronti, S., & Selva, M. (2007). Improving component substitution pansharpening through multivariate regression of MS +Pan data. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 3230-3239.
CNMF 2011 Python Matlab Yokoya, N., Yairi, T., & Iwasaki, A. (2011, July). Coupled non-negative matrix factorization (CNMF) for hyperspectral and multispectral data fusion: Application to pasture classification. In 2011 IEEE International Geoscience and Remote Sensing Symposium (pp. 1779-1782). IEEE.
GSOMP 2014 Matlab Akhtar, N., Shafait, F., & Mian, A. (2014, September). Sparse spatio-spectral representation for hyperspectral image super-resolution. In European conference on computer vision (pp. 63-78). Springer, Cham.
HySure 2014 Matlab Simoes, M., Bioucas-Dias, J., Almeida, L. B., & Chanussot, J. (2014, October). Hyperspectral image superresolution: An edge-preserving convex formulation.Hysure In 2014 IEEE International Conference on Image Processing (ICIP) (pp. 4166-4170). IEEE.
BayesianSparse (very slow) 2015 Matlab Akhtar, N., Shafait, F., & Mian, A. (2015). Bayesian sparse representation for hyperspectral image super resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3631-3640).
FUSE 2015 Matlab Wei, Q., Dobigeon, N., & Tourneret, J. Y. (2015). Bayesian fusion of multi-band images. IEEE Journal of Selected Topics in Signal Processing, 9(6), 1117-1127.
SupResPALM 2015 Matlab Lanaras, C., Baltsavias, E., & Schindler, K. (2015). Hyperspectral super-resolution by coupled spectral unmixing. In Proceedings of the IEEE international conference on computer vision (pp. 3586-3594).
NSSR 2016 Matlab Dong, W., Fu, F., Shi, G., Cao, X., Wu, J., Li, G., & Li, X. (2016). Hyperspectral image super-resolution via non-negative structured sparse representation. IEEE Transactions on Image Processing, 25(5), 2337-2352.
CNN-FUS 2018 Matlab Dian, R., Li, S., & Kang, X. (2020). Regularizing hyperspectral and multispectral image fusion by CNN denoiser. IEEE transactions on neural networks and learning systems, 32(3), 1124-1135.
CSTF (unstable) 2018 Matlab Li, S., Dian, R., Fang, L., & Bioucas-Dias, J. M. (2018). Fusing hyperspectral and multispectral images via coupled sparse tensor factorization. IEEE Transactions on Image Processing, 27(8), 4118-4130.
LTMR 2019 Matlab Dian, R., & Li, S. (2019). Hyperspectral image super-resolution via subspace-based low tensor multi-rank regularization. IEEE Transactions on Image Processing, 28(10), 5135-5146.
LTTR 2019 Matlab Dian, R., Li, S., & Fang, L. (2019). Learning a low tensor-train rank representation for hyperspectral image super-resolution. IEEE transactions on neural networks and learning systems, 30(9), 2672-2683.

* pan-sharpening methods adapted to HS–MS fusion [^1] via hypersharpening [^2].

Other Methods

Code is available but wrapper is not implemented yet.

Method Year Code Paper
MF 2011 Matlab Kawakami, R., Matsushita, Y., Wright, J., Ben-Ezra, M., Tai, Y. W., & Ikeuchi, K. (2011, June). High-resolution hyperspectral imaging via matrix factorization. In CVPR 2011 (pp. 2329-2336). IEEE.
SNMF 2013 Matlab Wycoff, E., Chan, T. H., Jia, K., Ma, W. K., & Ma, Y. (2013, May). A non-negative sparse promoting algorithm for high resolution hyperspectral imaging. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1409-1413). IEEE.
BSR 2015 Matlab Wei, Q., Bioucas-Dias, J., Dobigeon, N., & Tourneret, J. Y. (2015). Hyperspectral and multispectral image fusion based on a sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3658-3668.
BlindFuse 2016 Matlab Wei, Q., Bioucas-Dias, J., Dobigeon, N., Tourneret, J. Y., & Godsill, S. (2016, September). Blind model-based fusion of multi-band and panchromatic images. In 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) (pp. 21-25). IEEE.
FUMI 2016 Matlab Wei, Q., Bioucas-Dias, J., Dobigeon, N., Tourneret, J. Y., Chen, M., & Godsill, S. (2016). Multiband image fusion based on spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7236-7249.
MSDCNN * 2017 Python Yuan, Q., Wei, Y., Meng, X., Shen, H., & Zhang, L. (2018). A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(3), 978-989.
BRS 2018 Matlab Bungert, L., Coomes, D. A., Ehrhardt, M. J., Rasch, J., Reisenhofer, R., & Schönlieb, C. B. (2018). Blind image fusion for hyperspectral imaging with the directional total variation. Inverse Problems, 34(4), 044003.
CMS 2018 Matlab Zhang, L., Wei, W., Bai, C., Gao, Y., & Zhang, Y. (2018). Exploiting clustering manifold structure for hyperspectral imagery super-resolution. IEEE Transactions on Image Processing, 27(12), 5969-5982.
DHSIS 2018 Python Dian, R., Li, S., Guo, A., & Fang, L. (2018). Deep hyperspectral image sharpening. IEEE transactions on neural networks and learning systems, 29(11), 5345-5355.
SSF-CNN & PDCon-SSF * 2018 Python Han, X. H., Shi, B., & Zheng, Y. (2018, October). SSF-CNN: Spatial and spectral fusion with CNN for hyperspectral image super-resolution. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 2506-2510). IEEE.
STEREO 2018 Matlab Kanatsoulis, C. I., Fu, X., Sidiropoulos, N. D., & Ma, W. K. (2018). Hyperspectral super-resolution: A coupled tensor factorization approach. IEEE Transactions on Signal Processing, 66(24), 6503-6517.
uSDN 2018 Python Qu, Y., Qi, H., & Kwan, C. (2018). Unsupervised sparse dirichlet-net for hyperspectral image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2511-2520).
DBIN 2019 Python Wang, W., Zeng, W., Huang, Y., Ding, X., & Paisley, J. (2019). Deep blind hyperspectral image fusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4150-4159).
CUCaNet 2020 Python Yao, J., Hong, D., Chanussot, J., Meng, D., Zhu, X., & Xu, Z. (2020, August). Cross-attention in coupled unmixing nets for unsupervised hyperspectral super-resolution. In European Conference on Computer Vision (pp. 208-224). Springer, Cham.
GDD 2020 Python Uezato, T., Hong, D., Yokoya, N., & He, W. (2020, August). Guided deep decoder: Unsupervised image pair fusion. In European Conference on Computer Vision (pp. 87-102). Springer, Cham.
TFNet & ResTFNet * 2020 Python Liu, X., Liu, Q., & Wang, Y. (2020). Remote sensing image fusion based on two-stream fusion network. Information Fusion, 55, 1-15.
MHF-net 2020 Python Xie, Q., Zhou, M., Zhao, Q., Xu, Z., & Meng, D. (2020). MHF-net: An interpretable deep network for multispectral and hyperspectral image fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence.
PZRes-Net 2020 Python Zhu, Z., Hou, J., Chen, J., Zeng, H., & Zhou, J. (2020). Hyperspectral image super-resolution via deep progressive zero-centric residual learning. IEEE Transactions on Image Processing, 30, 1423-1438.
RecHSISR 2020 Python Wei, W., Nie, J., Zhang, L., & Zhang, Y. (2020). Unsupervised recurrent hyperspectral imagery super-resolution using pixel-aware refinement. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15.
SSRNET 2020 Python Zhang, X., Huang, W., Wang, Q., & Li, X. (2020). SSR-NET: Spatial–spectral reconstruction network for hyperspectral and multispectral image fusion. IEEE Transactions on Geoscience and Remote Sensing, 59(7), 5953-5965.
TONWMD 2020 Python Shen, D., Liu, J., Xiao, Z., Yang, J., & Xiao, L. (2020). A twice optimizing net with matrix decomposition for hyperspectral and multispectral image fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4095-4110.
Two-CNN 2020 Matlab Yang, J., Zhao, Y. Q., & Chan, J. C. W. (2018). Hyperspectral and multispectral image fusion via deep two-branches convolutional neural network. Remote Sensing, 10(5), 800.
UAL 2020 Python Zhang, L., Nie, J., Wei, W., Zhang, Y., Liao, S., & Shao, L. (2020). Unsupervised adaptation learning for hyperspectral imagery super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3073-3082).
ADMM-HFNET 2021 Python Shen, D., Liu, J., Wu, Z., Yang, J., & Xiao, L. (2021). ADMM-HFNet: A Matrix Decomposition-Based Deep Approach for Hyperspectral Image Fusion. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-17.
Fusformer 2021 Python Hu, J. F., Huang, T. Z., & Deng, L. J. (2021). Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution. arXiv preprint arXiv:2109.02079.
MoG-DCN 2021 Python Dong, W., Zhou, C., Wu, F., Wu, J., Shi, G., & Li, X. (2021). Model-guided deep hyperspectral image super-resolution. IEEE Transactions on Image Processing, 30, 5754-5768.
HyperFusion 2021 Python Tian, X., Zhang, W., Chen, Y., Wang, Z., & Ma, J. (2021). Hyperfusion: A computational approach for hyperspectral, multispectral, and panchromatic image fusion. IEEE Transactions on Geoscience and Remote Sensing.
HSRnet 2021 Python Dong, W., Zhou, C., Wu, F., Wu, J., Shi, G., & Li, X. (2021). Model-guided deep hyperspectral image super-resolution. IEEE Transactions on Image Processing, 30, 5754-5768.
TSFN 2021 Python Wang, X., Chen, J., Wei, Q., & Richard, C. (2021). Hyperspectral Image Super-Resolution via Deep Prior Regularization with Parameter Estimation. IEEE Transactions on Circuits and Systems for Video Technology.
u2MDN 2021 Python Qu, Y., Qi, H., Kwan, C., Yokoya, N., & Chanussot, J. (2021). Unsupervised and unregistered hyperspectral image super-resolution with mutual Dirichlet-Net. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-18.
DBSR 2022 Python Zhang, L., Nie, J., Wei, W., Li, Y., & Zhang, Y. (2020). Deep blind hyperspectral image super-resolution. IEEE Transactions on Neural Networks and Learning Systems, 32(6), 2388-2400.
DHIF 2022 Python Huang, T., Dong, W., Wu, J., Li, L., Li, X., & Shi, G. (2022). Deep Hyperspectral Image Fusion Network With Iterative Spatio-Spectral Regularization. IEEE Transactions on Computational Imaging, 8, 201-214.
HSI-CSR 2022 Caffe Fu, Y., Zhang, T., Zheng, Y., Zhang, D., & Huang, H. (2019). Hyperspectral image super-resolution with optimized RGB guidance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11661-11670).
RGBaux 2022 Python Li, K., Dai, D., & Van Gool, L. (2022). Hyperspectral Image Super-Resolution with RGB Image Super-Resolution as an Auxiliary Task. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 3193-3202).
MIAE 2022 Python Liu, J., Wu, Z., Xiao, L., & Wu, X. J. (2022). Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution. IEEE Transactions on Geoscience and Remote Sensing.
NonRegSRNet 2022 Python Zheng, K., Gao, L., Hong, D., Zhang, B., & Chanussot, J. (2021). NonRegSRNet: A Nonrigid Registration Hyperspectral Super-Resolution Network. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16.
RAFnet 2022 Python Lu, R., Chen, B., Cheng, Z., & Wang, P. (2020). RAFnet: Recurrent attention fusion network of hyperspectral and multispectral images. Signal Processing, 177, 107737.
SpfNet 2022 Python Liu, J., Shen, D., Wu, Z., Xiao, L., Sun, J., & Yan, H. (2022). Patch-Aware Deep Hyperspectral and Multispectral Image Fusion by Unfolding Subspace-Based Optimization Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
UDALN 2022 Python Li, J., Zheng, K., Yao, J., Gao, L., & Hong, D. (2022). Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.

* code available in another repo (from a different paper)

Extensions

Extensions of HSI methods with publicly available code. These should be regarded as extensions to the base pipelines and not as a separate methods. These take as input a super-resolution image (output of the HSI method) together with the MS and HS images (original HSI method input); and provide as input an improved super-resolution image. The wrappers for these extensions are not implemented in this repository yet.

extended-diagram

Method Year Code Paper
TVTVHS 2021 Python Vella, M., Zhang, B., Chen, W., & Mota, J. F. (2021, September). Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization. In 2021 IEEE International Conference on Image Processing (ICIP) (pp. 3837-3841). IEEE.
DeepGrad 2022 Matlab Wang, X., Chen, J., & Richard, C. (2022). Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion. arXiv preprint arXiv:2201.09851.

Metrics

To evaluate the quality of the methods, the output of the superresolution methods is compared with the ground truth of the dataset. We compute several metrics (listed below) using sewar.

Acronym Full Name Paper
RMSE Root Mean Squared Error -
PSNR Peak Signal-to-Noise Ratio Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600-612.
SSIM Structural Similarity Index Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600-612.
UQI Universal Quality Image Index Wang, Z., & Bovik, A. C. (2002). A universal image quality index. IEEE signal processing letters, 9(3), 81-84.
MS-SSIM Multi-scale Structural Similarity Index Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003, November). Multiscale structural similarity for image quality assessment. In The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 (Vol. 2, pp. 1398-1402). Ieee.
ERGAS Erreur Relative Globale Adimensionnelle de Synthèse Wald, L. (2000, January). Quality of high resolution synthesised images: Is there a simple criterion?. In Third conference" Fusion of Earth data: merging point measurements, raster maps and remotely sensed images" (pp. 99-103). SEE/URISCA.
SCC Spatial Correlation Coefficient Zhou, J., Civco, D. L., & Silander, J. A. (1998). A wavelet transform method to merge Landsat TM and SPOT panchromatic data. International journal of remote sensing, 19(4), 743-757.
RASE Relative Average Spectral Error González-Audícana, M., Saleta, J. L., Catalán, R. G., & García, R. (2004). Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Transactions on Geoscience and Remote sensing, 42(6), 1291-1299.
SAM Spectral Angle Mapper Yuhas, R. H., Goetz, A. F., & Boardman, J. W. (1992, June). Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm. In JPL, Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop.
VIF Visual Information Fidelity Sheikh, H. R., & Bovik, A. C. (2006). Image information and visual quality. IEEE Transactions on image processing, 15(2), 430-444.
PSNR-B Block Sensitive - Peak Signal-to-Noise Ratio Yim, C., & Bovik, A. C. (2010). Quality assessment of deblocked images. IEEE Transactions on Image Processing, 20(1), 88-98.
Q2ⁿ * Q2ⁿ Garzelli, A., & Nencini, F. (2009). Hypercomplex quality assessment of multi/hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 6(4), 662-665.

* to be implemented in the future.

Requirements

[^1]: Yokoya, N., Grohnfeldt, C., & Chanussot, J. (2017). Hyperspectral and multispectral data fusion: A comparative review of the recent literature. IEEE Geoscience and Remote Sensing Magazine, 5(2), 29-56. [paper] [code]

[^2]: Selva, M., Aiazzi, B., Butera, F., Chiarantini, L., & Baronti, S. (2015). Hyper-sharpening: A first approach on SIM-GA data. IEEE Journal of selected topics in applied earth observations and remote sensing, 8(6), 3008-3024. [paper]

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🌇🌆 Benchmarking of Hyperspectral Image Fusion methods 🏙🌃

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