Title :
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
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;
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
DOI :
10.1109/ICCAE.2010.5451241
