Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network
Open accessAbstract
In this letter, we propose a method using a 3-D convolutional neural network to fuse together multispectral and hyperspectral (HS) images to obtain a high resolution HS image. Dimensionality reduction of the HS image is performed prior to fusion in order to significantly reduce the computational time and make the method more robust to noise. Experiments are performed on a data set simulated using a real HS image. The results obtained show that the proposed approach is very promising when compared with conventional methods. This is especially true when the HS image is corrupted by additive noise.
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The most recent ways involving classification using hyperspectral data include extreme learning machine (ELM), partial least squares discriminant analysis (PLS-DA), and various deep learning algorithms.
“The most recent ways involving classification using hyperspectral data include extreme learning machine (ELM) [7–9], partial least squares discriminant analysis (PLS-DA) [10,11], and various deep learning algorithms [12,13].”
From Development of online classification system for construction waste based on industrial camera and hyperspectral camera (2019) · cites this paper as [13]
The Introduction (p. 1) describes a deep-learning method for fusing multispectral and hyperspectral images, so the paper supports the deep-learning portion of the citation. It identifies classification as a possible use of hyperspectral information but does not present its method as hyperspectral classification; it does not support that qualification. (LLM, conf=0.88)
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · see citing works
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Research works printed but not linked: 17
- [1][1] G. Vivone, L. Alparone, J. Chanussot, M. Dalla Mura, A. Garzelli, G. Licciardi, R. Restaino, and L. Wald, 'A critical comparison among pansharpening algorithms,' IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 5, pp. 2565-2586, May 2015. -
- [2][2] F. Palsson, J. R. Sveinsson, and M. O. Ulfarsson, 'A new pansharpening algorithm based on total variation,' IEEE Geoscience and Remote Sensing Letters , vol. 11, no. 1, pp. 318-322, Jan. 2014. -
- [3][3] X. He, L. Condat, J. Bioucas-Dias, J. Chanussot, and J. Xia, 'A new pansharpening method based on spatial and spectral sparsity priors,' IEEE Transactions on Image Processing , vol. 23, no. 9, pp. 4160-4174, Sept. 2014. -
- [4][4] Y. Zhang, S. De Backer, and P. Scheunders, 'Noise-resistant wavelet-based bayesian fusion of multispectral and hyperspectral images,' IEEE Transactions on Geoscience and Remote Sensing , vol. 47, no. 11, pp. 3834-3843, Nov. 2009. -
- [5][5] F. Palsson, J. R. Sveinsson, M. O. Ulfarsson, and J. A. Benediktsson, 'Model-based fusion of multi- and hyperspectral images using PCA and wavelets,' IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 5, pp. 2652-2663, May 2015. -
- [6][6] Z. H. Nezhad, A. Karami, R. Heylen, and P. Scheunders, 'Fusion of hyperspectral and multispectral images using spectral unmixing and sparse coding,' IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 9, no. 6, pp. 2377-2389, June 2016. -
- [7][7] Y. Zhang, Y. Wang, Y. Liu, C. Zhang, M. He, and S. Mei, 'Hyperspectral and multispectral image fusion using CNMF with minimum endmember simplex volume and abundance sparsity constraints,' in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , July 2015, pp. 1929-1932. -
- [8][8] Y. Zhang and M. He, 'Multi-spectral and hyperspectral image fusion using 3-d wavelet transform,' Journal of Electronics (China) , vol. 24, no. 2, pp. 218-224, 2007. -
- [9][9] D. Sylla, A. Minghelli-Roman, P. Blanc, A. Mangin, and O. H. F. d'Andon, 'Fusion of multispectral images by extension of the pan-sharpening arsis method,' IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 7, no. 5, pp. 1781-1791, May 2014. -
- [10][10] Z. Chen, H. Pu, B. Wang, and G. M. Jiang, 'Fusion of hyperspectral and multispectral images: A novel framework based on generalization of pan-sharpening methods,' IEEE Geoscience and Remote Sensing Letters , vol. 11, no. 8, pp. 1418-1422, Aug 2014. -
- [11][11] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, 'Going deeper with convolutions,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 1-9. -
- [13][13] W. Huang, L. Xiao, Z. Wei, H. Liu, and S. Tang, 'A new pan-sharpening method with deep neural networks,' IEEE Geoscience and Remote Sensing Letters , vol. 12, no. 5, pp. 10371041, May 2015. -
- [14][14] G. Masi, D. Cozzolino, L. Verdoliva, and G. Scarpa, 'Pansharpening by convolutional neural networks,' Remote Sensing , vol. 8, no. 7, pp. 594:1-22, 2016. -
- [15][15] I. Jolliffe, Principal component analysis , 2nd ed. Springer, 2002. -
- [17][17] A. Graves, A. r. Mohamed, and G. Hinton, 'Speech recognition with deep recurrent neural networks,' in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing , May 2013, pp. 6645-6649. -
- [19][19] L. Wald, 'Quality of high resolution synthesized images: Is there a simple criterion?' in In Proceedings of the third conference 'Fusion of Earth data: merging point measurements, raster maps and remotely sensed images' . SEE/URISCA, 2000. -
- [20][20] R. H. Yuhas, A. F. Goetz, and J. W. Boardman, 'Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm,' in Summaries of the 3rd Annual JPL Airborne Geoscience Workshop , vol. 1, 1992, pp. 147-149. -
Cited by
Sorted by citations
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456 · see citing works
Pixel-wise regression using U-Net and its application on pansharpening
Yao, W., Zeng, Z., Lian, C., & Tang, H. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.103 · Cited by 133 · see citing works
Xiao, W., Yang, J., Fang, H., Zhuang, J., & Ku, Y. (2019) · PLOS ONE · doi:10.1371/journal.pone.0208706 · Cited by 27 · see citing works
- Authors
- Palsson, F., Sveinsson, J. R., & Ulfarsson, M. O.
- Affiliations
- University of Iceland (IS)
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2017-05
- Volume
- 14
- Issue
- 5
- Pages
- 639-643
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2592312604
- Cited by
- 364
- References
- 3
- Citations by year
- 2026: 24 · 2025: 40 · 2024: 39 · 2023: 47 · 2022: 61 · 2021: 51 · 2020: 45 · 2019: 33 · 2018: 20 · 2017: 4
- FWCI
- 23.16
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Geoscience and Remote Sensing Letters (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Icelandic Research Fund (174075-05)
- University of Iceland Research Fund
- Topics
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Engineering › Ocean Engineering › Satellite Image Processing and Photogrammetry
- Keywords
- hyperspectral image fusion, multispectral image fusion, 3D convolutional neural networks, dimensionality reduction, noise robustness, computational efficiency
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