Remote sensing data fusion using support vector machine

Author

Zhao, Shuhe

Author_Institution

Inst. of Remote Sensing & GIS, Peking Univ., Beijing, China

Volume

4

fYear

2004

fDate

20-24 Sept. 2004

Firstpage

2575

Abstract

The paper introduces a support vector machine (SVM) into research of remote sensing data fusion, and a new approach of remote sensing data fusion based on SVM is presented. In the selected test area in Shaoxing City, Zhejiang Province, China, a data fusion experiment was conducted using Landsat TM multispectral data (30 m) and SPOT-4 panchromatic (PAN) data (10 m). The results show that the overall classification accuracy of the fusion data reached 76.8%. The new fusion method could detect effectively the ground objects, which have close spectrum.

Keywords

geophysical techniques; image classification; remote sensing; sensor fusion; support vector machines; China; Landsat TM multispectral data; SPOT-4 panchromatic data; Shaoxing City; Zhejiang Province; classification accuracy; close spectrum; data fusion; fusion method; ground objects; image classification; learning machine; remote sensing; support vector machine; Cities and towns; Geographic Information Systems; Kernel; Machine learning; Remote sensing; Satellites; Sensor fusion; Support vector machine classification; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International

Print_ISBN

0-7803-8742-2

Type

conf

DOI

10.1109/IGARSS.2004.1369823

Filename

1369823