Pattern classification for remote sensing using neural network

Author

Omatu, Sigeru ; Yoshida, Tomoji

Author_Institution

Dept. of Inf. Sci. & Intelligent Syst., Tokushima Univ., Japan

fYear

1993

fDate

18-21 Aug 1993

Firstpage

899

Abstract

Proposes a pattern classification method for remote sensing data based on neural network theory. From Kohonen´s self-organizing feature maps, training areas for each pattern are selected. Using the back propagation algorithm, the layered neural network is trained such that the training patterns can be classified within a level. The experiments on LANDSAT TM data show that this approach produces excellent classification results compared with the conventional Bayesian approach

Keywords

environmental science computing; image recognition; learning (artificial intelligence); pattern recognition; remote sensing; self-organising feature maps; Kohonen self-organizing feature maps; LANDSAT TM data; back propagation algorithm; layered neural network; neural network; pattern classification method; remote sensing; training areas; Bayesian methods; Biological neural networks; Data analysis; Image processing; Military computing; Neural networks; Pattern classification; Remote sensing; Satellites; Space technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1993. IGARSS '93. Better Understanding of Earth Environment., International

Conference_Location

Tokyo

Print_ISBN

0-7803-1240-6

Type

conf

DOI

10.1109/IGARSS.1993.322177

Filename

322177