Paper title:

Segmentation Method for Hyperspectral Images using Tensor Decomposition

Published in: Issue 2, (Vol. 14) / 2020
Publishing date: 2020-07-14
Pages: 9-14
Author(s): BILIUS Laura-Bianca, PENTIUC Stefan-Gheorghe
Abstract. Hyperspectral Images are a challenging area because of big dimensions, spatial and spectral low resolution. This paper aims to propose a method to provide an accurate and quick classification based on tensor decomposition and map segmentation. For reducing the dimensions of hyperspectral data and obtain the abundances map we used Parafac decomposition. For determining the segmentation abundances map representing the segmentation abundances matrix, an algorithm based on the Dynamic Clustering Algorithm [1] principle was used having as input the estimated abundances matrix. The segmentation abundances map was visually labeled using the true color image.
Keywords: Tensor Decomposition, Parafac, Segmentation, Hyperspectral Images

1. Edwin Diday, “The dynamic clusters method in nonhierarchical clustering”, March 1973, International Journal of Parallel Programming 2(1):61-88 DOI: 10.1007/BF00987153.

2. Y. Y. Tang, Y. Lu and H. Yuan, "Hyperspectral Image Classification Based on Three-Dimensional Scattering Wavelet Transform," in IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 5, pp. 2467-2480, May 2015, doi: 10.1109/TGRS.2014.2360672.

3. N. Keshava and J. F. Mustard, "Spectral unmixing," in IEEE Signal Processing Magazine, vol. 19, no. 1, pp. 44-57, Jan. 2002, doi: 10.1109/79.974727.

4. Y. Qian, F. Xiong, S. Zeng, J. Zhou and Y. Y. Tang, "Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery," in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 3, pp. 1776-1792, March 2017, doi: 10.1109/TGRS.2016.2633279.

5. Jose Manuel Amigo, “Hyperspectral Imaging, Volume 32, 1st Edition”, Elsevier, ISBN: 9780444639776, eBook ISBN: 9780444639783.

6. S. Ranjan, D. R. Nayak, K. S. Kumar, R. Dash and B. Majhi, "Hyperspectral image classification: A k-means clustering based approach," 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, 2017, pp. 1-7, doi: 10.1109/ICACCS.2017.8014707.

7. A., Sahar. (2016). Hyperspectral Image Classification Using Unsupervised Algorithms. International Journal of Advanced Computer Science and Applications. 7. 10.14569/IJACSA.2016.070425.

8. Alturki, Arwa and Ouiem Bchir. “Clustering Hyperspectral Data.” (2017), DOI:10.5121/csit.2017.70508.

9. Guangzhe Zhao, Bing Tu, Hongyan Fei, Nanying Li, Xianchang Yang, Spatial-spectral classification of hyperspectral image via group tensor decomposition, Neurocomputing, Vol. 316, 2018, Pp 68-77, ISSN 0925-2312, (

10. J. Chen, W. Zhang, Y. Qian and M. Ye, "Deep Tensor Factorization for Hyperspectral Image Classification," IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018, pp. 4788-4791, doi: 10.1109/IGARSS.2018.8517386.

11. Dylan Anderson, Aleksander Bapst, Joshua Coon, Aaron Pung, and Michael Kudenov "Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination", Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Computer Science Section,

Ultraspectral Imagery XXIII, 101980Q (5 May 2017);

12. B. Luo and J. Chanussot, "Unsupervised classification of hyperspectral images by using linear unmixing algorithm," 2009 16th IEEE International Conference on Image Processing (ICIP), Cairo, 2009, pp. 2877-2880, doi: 10.1109/ICIP.2009.5413491.

13. N. Dobigeon, Y. Altmann, N. Brun, S. Moussaoui, Chapter 6 - Linear and Nonlinear Unmixing in Hyperspectral Imaging, Editor(s): Cyril Ruckebusch, Data Handling in Science and Technology, Elsevier, Volume 30, 2016, Pages 185-224, ISSN 0922-3487, ISBN 9780444636386,

14. X. Zhang, G. Wen, B. Hui and W. Dai, "A batch-wise segmentation algorithm for hyperspectral images," 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, 2016, pp. 1-4, doi: 10.1109/WHISPERS.2016.8071772.

15. Zhu, Feiyun. “Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey.” arXiv: Computer Vision and Pattern Recognition (2017): n. pag.

16. Jouni M., Dalla Mura M., Comon P. (2019) Classification of Hyperspectral Images as Tensors Using Nonnegative CP Decomposition. In: Burgeth B., Kleefeld A., Naegel B., Passat N., Perret B. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2019. Lecture Notes in Computer Science, vol 11564. Springer, Cham, DOI:

17. Pei Liang and YanChunyu, "Study on mixed pixel classification method of remote sensing image based on fuzzy theory," 2009 Joint Urban Remote Sensing Event, Shanghai, 2009, pp. 1-7, doi: 10.1109/URS.2009.5137572.

18. T. G. Kolda, B. W. Bader. Tensor Decompositions and Applications. SIAM Review, Vol. 51, No. 3, pp. 455-500, 2009.

19. Yang, Bo & Zamzam, Ahmed & Sidiropoulos, N.D.. (2018). ParaSketch: Parallel Tensor Factorization via Sketching. 10.1137/1.9781611975321.45.

20. Krig S. (2014) Ground Truth Data, Content, Metrics, and Analysis. In: Computer Vision Metrics. Apress, Berkeley, CA; DOI:, Print ISBN: 978-1-4302-5929-9; Online ISBN: 978-1-4302-5930-5.

21. | Science for a changing world, 2020,

22. Google Maps. 2020. Google Maps. online Available at:

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