Using Wavelet Support Vector Machine for Classification of Hyperspectral Images

被引:2
作者
Banki, Mohammad Hossein [1 ]
Shirazi, Ali Asghar Beheshti [1 ]
机构
[1] Iran Univ Sci & Technol, Dept Elect Engn, Tehran, Iran
来源
2009 SECOND INTERNATIONAL CONFERENCE ON MACHINE VISION, PROCEEDINGS, ( ICMV 2009) | 2009年
关键词
Hyperspectral image processing; Classification; SVM; Wavelet Kernel;
D O I
10.1109/ICMV.2009.64
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Support Vector Machine (SVM) is a machine learning algorithm, which has been used recently for classification of hyperspectral images. SVM uses various kernel functions like RBF and polynomial to map the data into higher dimensional space to improve data separability. New kernel functions are used in this paper to classify hyperspectral images which are based on wavelet functions as named Wavelet-kernels. The experimental results indicate that Wavelet-kernels provide better classification accuracy than previous kernels.
引用
收藏
页码:154 / 157
页数:4
相关论文
共 11 条
[1]  
[Anonymous], AVIRIS NW Indiana's Indian Pines 1992 Data Set
[2]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[3]   THE WAVELET TRANSFORM, TIME-FREQUENCY LOCALIZATION AND SIGNAL ANALYSIS [J].
DAUBECHIES, I .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1990, 36 (05) :961-1005
[4]   Hyperspectral image classification using relevance vector machines [J].
Demir, Beguem ;
Erturk, Sarp .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2007, 4 (04) :586-590
[5]  
FAUVEL M, EURASIP J ADV SIGNAL, V2009
[6]   Support vector machines for hyperspectral remote sensing classification [J].
Gualtieri, JA ;
Cromp, RF .
ADVANCES IN COMPUTER-ASSISTED RECOGNITION, 1999, 3584 :221-232
[7]   Classification of hyperspectral remote sensing images with support vector machines [J].
Melgani, F ;
Bruzzone, L .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2004, 42 (08) :1778-1790
[8]  
Schlkopf B., 2002, Learning with kernels: Support vector machines, regularization, optimization, and beyond
[9]  
Vapnik V., 1998, STAT LEARNING THEORY
[10]  
Wu FF, 2005, LECT NOTES ARTIF INT, V3789, P462