PERFORMANCE OF THE SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORK CLASSIFIERS FOR ROADS IDENTIFICATION

The objective of this project was to compare two non-parametric classification methods (“Support Vector Machine” – SVM and “Artificial Neural Networks” – ANN) of road regions in high spatial resolution images and associated with data from Airborne Laser Scanning. The study aims to verify what kind o...

Full description

Saved in:

Bibliographic Details
Published in:	International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLIII-B3-2021; pp. 9 - 14
Main Authors:	Andrade, A. C., Alixandrini Jr, M. J., Carvalho, F. P. S., Fernandes, V. O.
Format:	Journal Article Conference Proceeding
Language:	English
Published:	Gottingen Copernicus GmbH 28-06-2021 Copernicus Publications
Subjects:	Airborne lasers Airborne observation Artificial neural networks Classification Classifiers Digital imaging Elevation Lasers Neural networks Parameters Spatial resolution Support vector machines Training
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The objective of this project was to compare two non-parametric classification methods (“Support Vector Machine” – SVM and “Artificial Neural Networks” – ANN) of road regions in high spatial resolution images and associated with data from Airborne Laser Scanning. The study aims to verify what kind of influence the layers of attributes have on the performance from respective classifiers: SVM and RNA. Our method based on tests of this classifiers on 4 bands of airborne images and normalization of the digital surface model (DSM) for showing only information on objects height in relation to ground and not of these in relation to the ground and relief, generating band 5. The samples were used to train chosen non-parametric classifiers (training sets for each different input image/landscape). All classifications had the same set of training samples and the same classification parameters. The optimal parameters for classifications were obtained through the existing library in the Weka mining package: LibSVM and LibMultiLayerPerceptron. Our results demonstrated the existence of a direct relationship between the elevation band of the targets in relation to the terrain (band 05) with the improvement of their performance and lower degree of between bands correlation can also be considered a factor that has a positive influence. As for Neural Networks, the experiment results demonstrate that the presence of the near infrared band (band 04) was decisive for the performance improving of certain combinations in relation to others.
ISSN:	2194-9034 1682-1750 2194-9034
DOI:	10.5194/isprs-archives-XLIII-B3-2021-9-2021