Abstract
The innate intricacy of hyperspectral images and the absence of the mark data set make the band selection a challenging task in hyperspectral imaging. Computational multifaceted nature can be decreased by distinguishing suitable bands and simultaneously optimizing the number of bands. The PSO (Particle swarm optimization) based technique is used for this purpose. Fitness function takes a significant role in PSO to make a balance between the optimal solution and the accuracy rate. Different distance metrics like Euclidean, City Block, etc. are used as fitness functions and the aftereffects of a similar investigation on different data sets are reported in the present paper.
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
Sun, T.-L., Chang, C.I., Du, Q., Althouse, M.L.G.: A joint band prioritization and band decorrelation approach to band selection for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 37(6), 2631–2641 (1999)
Huang, B., Gong, J., Zhang, L., Zhong, Y.: Dimensionality reduction based on clonal selection for hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 45(12), 4172–4186 (2007)
Zhang, X., Feng, J., Jiao, L.C., Sun, T.: Hyperspectral band selection based on trivariate mutual information and clonal selection. IEEE Trans. Geosci. Remote Sens. 52(7), 4092–4105 (2014)
Su, Genshe Chen Peijun Du Hongjun, Qian, Du: Optimized hyperspectral band selection using particle swarm optimization. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(6), 2659–2670 (2014)
Bai, Limin Shi Jun., Xiang, Shiming, Pan, Chunhong: Semisupervised pair-wise band selection for hyperspectral images. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 8(6), 2798–2813 (2015)
Chowdhury, A.R., Hazra, J., Dutta, P.: A hybrid approach for band selection of hyperspectral images. In: Hybrid Intelligence for Image Analysis and Understanding, Chapter 11, pp. 263–282. John Wiley and Sons Ltd. (2017)
Sotoca, J.M., Martinez-Uso, A., Pla, F., Garcia-Sevilla, P.: Clustering based hyperspectral band selection using information measures. IEEE Trans. Geosci. Remote Sens. 45(12), 4158–4171 (2007)
Gong, Yuan Yuan Maoguo, Zhang, Mingyang: Unsupervised band selection based on evolutionary multiobjective optimization for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 54(1), 544–557 (2016)
Rashwan, S., Dobigeon, N.: A split-and-merge approach for hyperspectral band selection. IEEE Geosci. Remote Sens. Lett. 14(8) (2017)
Chang, C.I., Wu, C.C., Liu, K.H., Chen, H.M., Chen, C.C.C., Wen, C.H.: Progressive band processing of linear spectral unmixing for hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 8(6), 2583–2597 (2015)
Zhu, Jingsheng Lei Zhongqin Bi Feifei Xu Guokang, Huang, Yuancheng: Unsupervised hyperspectral band selection by dominant set extraction. IEEE Trans. Geosci. Remote Sens. 54(1), 227–239 (2016)
Younan, Nicolas H., Yan, Xu, Qian, Du: Particle swarm optimization-based band selection for hyperspectral target detection. IEEE Geosci. Remote Sens. Lett. 14(4), 554–558 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chowdhury, A.R., Hazra, J., Dasgupta, K., Dutta, P. (2021). Comparative Study of the Effect of Different Fitness Functions in PSO Algorithm on Band Selection of Hyperspectral Imagery. In: Pan, I., Mukherjee, A., Piuri, V. (eds) Proceedings of Research and Applications in Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1355. Springer, Singapore. https://doi.org/10.1007/978-981-16-1543-6_9
Download citation
DOI: https://doi.org/10.1007/978-981-16-1543-6_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1542-9
Online ISBN: 978-981-16-1543-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)