Multi-class Classification for Wuhan Area´s TM Image Based on Support Vector Machine

Author

Liu, Liu ; Huang, Zhengjun ; Tan, Xiaojun ; Zeng, Zhiyuan

Author_Institution

Digital Eng. & Simulation Res. Center, Huazhong Univ. of Sci. & Technol., Wuhan, China

Volume

5

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

401

Lastpage

404

Abstract

This paper proposes a multi-class classification method based on Support Vector Machine (SVM), with an emphasis on classes of Wuhan area´s water resources. First, this method builds a SVM model by selecting proper testing sample data of Wuhan area´s TM image. Then, the image is classified as 5 classes based on the algorithm of SVM model. The experimental results show that this method has obvious advantages in accuracy, compared with the traditional method-Maximum likelihood, especially on classes of water resources.

Keywords

geography; image classification; support vector machines; water resources; TM image; Wuhan area; multiclass classification; support vector machine; water resources; Fuzzy systems; Image classification; Lakes; Machine learning algorithms; Quadratic programming; Remote sensing; Satellites; Support vector machine classification; Support vector machines; Water resources; Support Vector Machine (SVM); Wuhan area´s TM image; image classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3735-1

Type

conf

DOI

10.1109/FSKD.2009.48

Filename

5360591