Abstract
Hyperspectral Images (HSI) contain a lot of data channels. Due to their high dimensionality, it is difficult to design systems which are able to find optimal number of clusters to segment them. A qutrit based particle swarm optimization (PSO) for automatic clustering of hyperspectral images is introduced in this paper. A Band Fusion Technique is implemented by improving the Improved Subspace Decomposition Algorithm using SF Index as the fitness function. A new method for maintaining the superposition state of the qutrits is also successfully designed. A comparison with classical PSO is performed to clearly establish the supremacy of the proposed technique with respect to Peak signal-to-noise ratio (PSNR), Jaccard Index, Sørensen-Dice Similarity Index and the computational time. Finally, the unpaired two-tailed t test is conducted between the proposed technique and classical PSO, which renders better results for proposed qutrit based technique. The experiments are carried out on the Salinas Dataset. The proposed technique yields better results in all the tests conducted in comparison to the classical PSO.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
H. Cui, K. Zhang, Y. Fang, S. Sobolevsky, C. Ratti, B.K.P. Horn, A clustering validity index based on pairing frequency. IEEE Access 5, 24884–24894 (2017)
S. Dey, S. Bhattacharyya, U. Maulik, New quantum inspired meta-heuristic techniques for multi-level colour image thresholding. Appl. Soft Comput. 46, 677–702 (2016)
S. Dey, S. Bhattacharyya, U. Maulik, Efficient quantum inspired meta-heuristics for multi-level true colour image thresholding. Appl. Soft Comput. 56, 472–513 (2017)
S. Dey, S. Bhattacharyya, U. Maullik, Quantum-Inspired Automatic Clustering Technique Using Ant Colony Optimization Algorithm (IGI Global, 2018), pp. 27–54 (chap. 2)
N. Dhanachandra, K. Manglem, Y.J. Chanu, Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput. Sci. 54, 764–771 (2015)
L.R. Dice, Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
M. Dorigo, M. Birattari, Ant colony optimization. IEEE Comput. Intell. Mag. 1, 28–39 (2006)
J.C. Dunn, Well-separated clusters and optimal fuzzy partitions. J. Cybern. 4(1), 95–104 (1974)
A. Elmaizi, H. Nhaila, E. Sarhrouni, A. Hammouch, C. Nacir, A novel information gain based approach for classification and dimensionality reduction of hyperspectral images. Procedia Comput. Sci. 148, 126–134 (2019)
S. Fletcher, M.Z. Islam, Comparing sets of patterns with the Jaccard index. Australas. J. Inf. Syst. 22 (2018)
Y. Hariya, T. Kurihara, T. Shindo, K. Jin’no, L flight PSO, in 2015 IEEE Congress on Evolutionary Computation (CEC) (2015), pp. 2678–2684
A. Hore, D. Ziou, Image quality metrics: PSNR vs. SSIM, in 2010 20th International Conference on Pattern Recognition (2010), pp. 2366–2369. https://doi.org/10.1109/ICPR.2010.579
Hyperspectral Remote Sensing Scenes - Grupo de Inteligencia Computacional (GIC) (2019). http://www.ehu.eus/ccwintco/index.php?title=Hyperspectral_Remote_Sensing_Scenes [Online; Accessed 7 Oct 2019]
H. Jiang, S. Yi, J. Li, F. Yang, X. Hu, Ant clustering algorithm with k-harmonic means clustering. Expert Syst. Appl. 37(12), 8679–8684 (2010)
H. Li, H. He, Y. Wen, Dynamic particle swarm optimization and k-means clustering algorithm for image segmentation. Optik 126(24), 4817–4822 (2015)
U. Maulik, S. Bandyopadhyay, Performance evaluation of some clustering algorithms and validity indices. IEEE Trans. Pattern Anal. Mach. Intell. 24(12), 1650–1654 (2002)
D. McMahon, Quantum Computing Explained (Wiley, Hoboken, New Jersey, 2008)
A. Narayanan, M. Moore, Quantum-inspired genetic algorithms, in Proceedings of IEEE International Conference on Evolutionary Computation (1996), pp. 61–66
S. Saitta, B. Raphael, I.F.C. Smith, A bounded index for cluster validity, in Machine Learning and Data Mining in Pattern Recognition (Springer, Berlin, 2007), pp. 174–187
Y. Shi, R. Eberhart, A modified particle swarm optimizer, in 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) (1998), pp. 69–73
T. Valerii, Quantum genetic algorithm based on qutrits and its application. Math. Probl. Eng. 2018, 1–8 (2018). https://doi.org/10.1155/2018/8614073
F. Xie, F. Li, C. Lei, J. Yang, Y. Zhang, Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification. Appl. Soft Comput. 75, 428–440 (2019)
Acknowledgements
This work was supported by the AICTE sponsored RPS project on Automatic Clustering of Satellite Imagery using Quantum- Inspired Metaheuristics vide F.No 8-42/RIFD/RPS/Policy-1/2017-18.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Dutta, T., Dey, S., Bhattacharyya, S. (2020). Automatic Clustering of Hyperspectral Images Using Qutrit Based Particle Swarm Optimization. In: Bhattacharyya, S., Mitra, S., Dutta, P. (eds) Intelligence Enabled Research. Advances in Intelligent Systems and Computing, vol 1109. Springer, Singapore. https://doi.org/10.1007/978-981-15-2021-1_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-2021-1_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2020-4
Online ISBN: 978-981-15-2021-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)