Title

Multispectral landuse classification using neural networks and support vector machines: One or the other or both?

SelectedWorks Author Profiles:

Barnali Dixon

Document Type

Article

Publication Date

2008

Date Issued

January 2008

Date Available

June 2014

ISSN

0143-1161

Abstract

Land use classification is an important part of many remote sensing applications. A lot of research has gone into the application of statistical and neural network classifiers to remote-sensing images. This research involves the study and implementation of a new pattern recognition technique introduced within the framework of statistical learning theory called Support Vector Machines (SVMs), and its application to remote-sensing image classification. Standard classifiers such as Artificial Neural Network (ANN) need a number of training samples that exponentially increase with the dimension of the input feature space. With a limited number of training samples, the classification rate thus decreases as the dimensionality increases. SVMs are independent of the dimensionality of feature space as the main idea behind this classification technique is to separate the classes with a surface that maximizes the margin between them, using boundary pixels to create the decision surface. Results from SVMs are compared with traditional Maximum Likelihood Classification (MLC) and an ANN classifier. The findings suggest that the ANN and SVM classifiers perform better than the traditional MLC. The SVM and the ANN show comparable results. However, accuracy is dependent on factors such as the number of hidden nodes (in the case of ANN) and kernel parameters (in the case of SVM). The training time taken by the SVM is several magnitudes less.

Comments

Abstract only. Full-text article is available only through licensed access provided by the publisher. Published in International Journal of Remote Sensing, 29(4), 1185 - 1206. DOI: 10.1080/01431160701294661 Members of the USF System may access the full-text of the article through the authenticated link provided.

Language

en_US

Publisher

Taylor & Francis

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.