Application of neural network to identify the remote sensing data of hillslide

Author

Wang, Ting-shiuan ; Yu, Teng-to

Author_Institution

Dept. of Resource Eng., Nat. Cheng-Kung Univ., Tainan, Taiwan

Volume

2

fYear

2011

fDate

10-13 July 2011

Firstpage

661

Lastpage

665

Abstract

This study presents the results of neural network simulation of hillside area prediction from remote sensing data. Five neural network methods were compared, which were Back Propagation Network (BPN), Extend Neuron Networks (ENN), Fuzzy Neural Network (FNN), Analysis Adjustment Synthesis Network (AASN), and Genetic Algorithm Neural Network (GANN). Three factors were used as the predictor in this study, which were NDVI value, shape factor, and color difference. The result reveals that the BPN is the best choice, because the error is the lowest among the five schemes in this study.

Keywords

genetic algorithms; geophysics computing; image processing; neural nets; remote sensing; AASN; BPN; ENN; FNN; GANN; analysis adjustment synthesis network; back propagation network; extend neuron networks; fuzzy neural network; genetic algorithm neural network; hillside area prediction; hillslide; neural network simulation; remote sensing data; Biological neural networks; Cybernetics; Data models; Image color analysis; Machine learning; Remote sensing; Shape; Image identification; Image interpretation; Image variation; Neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016793

Filename

6016793