Multispectral Image Classification Using Multilayer Perceptron and Principal Components Analysis

Author

da Silva, Wanessa ; Habermann, Mateus ; Hideiti Shiguemori, Elcio ; do Livramento Andrade, Leidiane ; Morgado de Castro, Ruy

Author_Institution

Div. de Geointeligencia, Inst. de Estudos Avancados-IEAv, São José dos Campos, Brazil

fYear

2013

fDate

8-11 Sept. 2013

Firstpage

557

Lastpage

562

Abstract

This work presents a methodology for pattern classification from multispectral images acquired by the HSS airborne sensor. In order to achieve this purpose, a conjunction of Artificial Neural Network and Principal Components Analysis has been used. The results indicate that this approach can be alternatively employed in multispectral images to separate materials with specific characteristics based on their reflectance properties.

Keywords

geophysical image processing; image classification; multilayer perceptrons; principal component analysis; reflectivity; HSS airborne sensor; artificial neural network; multilayer perceptron; multispectral image classification; pattern classification; principal component analysis; reflectance properties; Artificial neural networks; Asphalt; Concrete; Principal component analysis; Remote sensing; Soil; Vectors; artificial neural network; hyperspectral scanner system; multispectral image; principal components analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on

Conference_Location

Ipojuca

Type

conf

DOI

10.1109/BRICS-CCI-CBIC.2013.98

Filename

6855907