Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 9,
  • Issue 1,
  • pp. 011003-
  • (2011)

Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classif ication

Not Accessible

Your library or personal account may give you access

Abstract

Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine (SVM) as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer II hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four principal components from principal component analysis and wavelet texture provides the highest accuracy (97.06%). Multi-kernel SVM is therefore an effective approach to improve the accuracy of hyperspectral image classification and to expand possibilities for remote sensing image interpretation and application.

© 2011 Chinese Optics Letters

PDF Article
More Like This
Feature weighting algorithms for classification of hyperspectral images using a support vector machine

Bin Qi, Chunhui Zhao, and Guisheng Yin
Appl. Opt. 53(13) 2839-2846 (2014)

Classification of water contamination developed by 2-D Gabor wavelet analysis and support vector machine based on fluorescence spectroscopy

P. Huang, T. Mao, Q. Yu, Y. Cao, J. Yu, G. Zhang, and D. Hou
Opt. Express 27(4) 5461-5477 (2019)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved