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An Unsupervised Learning Of Hyperspectral Images Using Fuzzy C-means (FCM) Clustering Method With Glowworm Swarm Optimization (GSO)

C. Rajinikanth, Dr. S. Abraham Lincon

Volume 4, Issue 4, Page 151-154, Year 2019 | DOI: 10.5185/amp.2019.0019

Keywords:

 Hyperspectral Images, Fuzzy C-means, Glowworm Swarm Optimization 

Abstract: 

The unsupervised learning method is one of the formidable operations in Hyper-Spectral Image (HSI) processing. Fuzzy C-Means (FCM) clustering is an optimistic and strategic method for selecting the unsupervised bands. There are some limits and standards in fuzzy clustering technique. The Glowworm Swarm Optimization (GSO) is proposed with combining fuzzy clustering and GSO. The GSO is introduced to enhance the performance of fuzzy clustering to optimize the characteristics of hyperspectral images. The main objective of the proposed method is to improve the accuracy of the hyperspectral datasets and to achieve it through better computational time. The experimental results are achieved through MATLAB toolbox and the proposed method has the capability to perform with the high quality hyperspectral image classification. Copyright © VBRI Press.

Advanced Materials Proceedings

The official journal of the International Association of Advanced Materials (IAAM)