A fuzzy neural network model (FNN model) for classification using Landsat-TM image data

Author

Zhang, Lijian ; Hoshi, Takashi

Author_Institution

Ibaraki Univ., Japan

Volume

3

fYear

1994

fDate

8-12 Aug 1994

Firstpage

1416

Abstract

A fuzzy backpropagation neural network model is proposed. In this model a fuzzy weighted distance (FWD) is used to interpret the degree of possibility of the input pattern belongs to a certain category in the fuzzy domain. An extended backpropagation (EBP) algorithm is employed to make the conventional multilayer perceptron (MLP) model can be used in fuzzy classification procedure. At the end, the Landsat-TM image data are used to inspect the effect of proposed model

Keywords

backpropagation; fuzzy neural nets; geophysical signal processing; geophysical techniques; image classification; image colour analysis; multilayer perceptrons; optical information processing; remote sensing; FNN model; Landsat-TM; category; extended backpropagation algorithm; fuzzy backpropagation neural network model; fuzzy neural network model; fuzzy weighted distance; geophysical measurement technique; image classification; input pattern; land surface terrain mapping; multilayer perceptron; neural net; optical imaging; remote sensing; visible multispectral method; Artificial neural networks; Data engineering; Fuzzy neural networks; Fuzzy sets; Multi-layer neural network; Multilayer perceptrons; Neural networks; Pattern recognition; Remote sensing; Satellites;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International

Conference_Location

Pasadena, CA

Print_ISBN

0-7803-1497-2

Type

conf

DOI

10.1109/IGARSS.1994.399456

Filename

399456