Title :
Determining an Efficient Supervised Classification Method for Hyperspectral Image
Author :
Joevivek, V. ; Hemalatha, T. ; Soman, K.P.
Author_Institution :
CEN, Amrita Univ., Coimbatore, India
Abstract :
This paper proposes a research work done in search of best-supervised learning algorithm and the best kernel for Hyperspectral Image classification. In this work, we find that SVM outperforms other supervised algorithms. Many kernels are utilized in support vector machines for classification. Among them Linear, Polynomial and RBF kernels are analysed and the kernel that best suits for the application is determined. Cuprite (Nevada, USA) is the Hyperspectral image used in this paper.
Keywords :
image classification; support vector machines; RBF kernels; best-supervised learning algorithm; efficient supervised classification; hyperspectral image classification; linear kernels; polynomial kernels; support vector machines; Classification algorithms; Earth; Hyperspectral imaging; Hyperspectral sensors; Kernel; Libraries; Minerals; Remote sensing; Support vector machine classification; Support vector machines;
Conference_Titel :
Advances in Recent Technologies in Communication and Computing, 2009. ARTCom '09. International Conference on
Conference_Location :
Kottayam, Kerala
Print_ISBN :
978-1-4244-5104-3
Electronic_ISBN :
978-0-7695-3845-7
DOI :
10.1109/ARTCom.2009.174
