Journal of Food, Agriculture and Environment

A novel optimization parameters of support vector machines model for the land use/ cover classification

Author(s):

Ying Liu ^{1, 2, 3}, Bai Zhang ¹*, Lihua Huang ¹, Limin Wang ²

Recieved Date: 2012-01-01, Accepted Date: 2012-04-20

Abstract:

Nowadays, support vector machines (SVM) are receiving increasing attention in land cover/use classification although one of the major drawbacks of the technique is the kernel function selection and its parameters setting. In this paper, a novel SVM parameters optimization method based on self- adaptive mutation particle swarm optimizer (SAMPSO-SVM) is proposed to improve the generalization performance of the SVM classifier. The SAMPSO algorithm, which is based on the variance of the population’s fitness, can break away the local optimum by the operation of self-adaptive mutation. Accordingly, very high classification accuracy will be achieved with the best value of the parameters of SVM, which have been searched using SAMPSO. In order to verify the validity of this SAMPSO-SVM method, a remote sensing land use/cover classification model is constructed using multi-spectral Landsat-5 TM data. In particular, they are organized so as to test the sensitivity of the SAMPSO-SVM model and that of the other reference classifiers used for comparison, i.e. maximum likelihood classifier (MLC), SVM classifier and standard PSO algorithm for SVM parameters optimization model (PSO-SVM). On an average, the SAMPSO–SVM model yielded an overall accuracy of 93.59% against 83.92% for maximum likelihood classier and outperformed PSO-SVM classier in terms of overall accuracy (by about 2%). The obtained results clearly confirm the effectiveness and robustness of the SAMPSO-SVM approach to the remote sensing land use/cover classification. 

Keywords:

Support vector machines, self-adaptive mutation, particle swarm optimization, land use/cover, classification

---

Journal: Journal of Food, Agriculture and Environment
Year: 2012
Volume: 10
Issue: 2
Category: Environment
Pages: 1098-1104

---

Full text for Subscribers

---

Information:

Note to users

The requested document is freely available only to subscribers/registered users with an online subscription to the Journal of Food, Agriculture & Environment. If you have set up a personal subscription to this title please enter your user name and password. All abstracts are available for free.

Article purchasing

If you like to purchase this specific document such as article, review or this journal issue, contact us. Specify the title of the article or review, issue, number, volume and date of the publication. Software and compilation, Science & Technology, all rights reserved. Your use of this website details or service is governed by terms of use. Authors are invited to check from time to time news or information.

---

Purchase this Article:   20 Purchase PDF Order Reprints for 15

---

Share this article :