New kernel function for hyperspectral image classification

Author

Banki, Mohammad Hossein ; Shirazi, Ali Asghar Beheshti

Author_Institution

Dept. of Electr. Eng., Iran Univ. of Sci. & Technol., Tehran, Iran

Volume

1

fYear

2010

fDate

26-28 Feb. 2010

Firstpage

780

Lastpage

783

Abstract

Support Vector Machines is a supervised classifier which used kernel functions to mitigate nonlinear problem. Various kernel functions like Gaussian and polynomial kernels previously used for hyperspectral image classification. In this paper, new kernel function is used for hyperspectral image classification. This kernel is based on wavelet which named wavelet-kernel. The comparative result of Wavelet kernel with two common kernels are given which shows wavelet kernel is a good choice for SVM classifier in remote sensing.

Keywords

image classification; remote sensing; support vector machines; wavelet transforms; Gaussian kernels; SVM classifier; hyperspectral image classification; kernel function; nonlinear problem; polynomial kernels; remote sensing; support vector machines; wavelet kernel; Hyperspectral imaging; Hyperspectral sensors; Image classification; Kernel; Machine learning; Pattern recognition; Polynomials; Remote sensing; Support vector machine classification; Support vector machines; Hyperspectral Images; Kernel Function; Mexican-hat Wavelet; Remote Sensing; SVM Classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-5585-0

Electronic_ISBN

978-1-4244-5586-7

Type

conf

DOI

10.1109/ICCAE.2010.5451241

Filename

5451241