Multilayer perceptron classification for ENVISAT-ASAR imagery

Author

Zhu, Feiya ; Guo, Huadong ; Dong, Qing ; Wang, Changlin

Author_Institution

Inst. of Remote Sensing Applications, Chinese Acad. of Sci., Beijing

Volume

5

fYear

2004

fDate

20-24 Sept. 2004

Firstpage

3077

Abstract

This paper describes the application of neural networks to targets classification from multi-polarization ENVISAT-ASAR imagery. The used neural network is multilayer perception (MLP) with fast learning (FL), which is fully interconnected network. Accordingly, the training data sets may be taken from a known truth data in the ground. And finally, the results of proposed method are compared with that of the other classification ones, the in situ test data are from Zhaoqing in Guangdong Province of China

Keywords

geophysical signal processing; geophysical techniques; image classification; learning (artificial intelligence); multilayer perceptrons; remote sensing by radar; synthetic aperture radar; China; Guangdong Province; Zhaoqing; fast learning; fully interconnected network; in situ test data; known truth data; multilayer perceptron classification; multipolarization ENVISAT-ASAR imagery; neural networks; target classification; Biological neural networks; Mathematical model; Multi-layer neural network; Multilayer perceptrons; Neural networks; Polynomials; Probability density function; Remote sensing; Testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Anchorage, AK

Print_ISBN

0-7803-8742-2

Type

conf

DOI

10.1109/IGARSS.2004.1370348

Filename

1370348