Fuzzy Computing in Data Science

1Band Reduction of HSI Segmentation Using FCM

V. Saravana Kumar¹*, S. Anantha Sivaprakasam², E.R. Naganathan³, Sunil Bhutada¹ and M. Kavitha⁴

¹ Department of IT, SreeNidhi Institute of Science and Technology, Hyderabad, India

² Department of CSE, Rajalakshmi Engineering College, Chennai, India

³ Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

⁴ Research Scholar, Manonmaniam Sundaranar University, Tirunelveli, India

Abstract

Hyperspectral has carried hundreds of nonoverlapping spectral channels of a specified scene, clustering is one of the approaches for diminishing the size of these large data sets. Segmentation is intricate for the raw data; however, it is likely for the reduced band of HSI. To lessen the band size, the classical clustering methods for example K-means, Fuzzy C-means are accomplished. An integrated image segmentation procedure built on interband clustering and intraband clustering is proposed. The interband clustering is performed by K-means clustering and Fuzzy C-means clustering algorithms, despite the fact the intraband clustering is executed using particle swarm segmentation (PSO) clustering algorithm. The performance of the K-means algorithm is subject to initial cluster centers. Besides, the final partition should be contingent on the initial configuration. The clustering consequences have profoundly been subject to the number of clusters stated. It is essential to provide refined direction for defining the number of ...