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Proceedings Paper

Classifying auroras using artificial neural networks
Author(s): Peter Rydesater; Urban Brandstrom; Ake Steen; Bjorn Gustavsson
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Paper Abstract

In Auroral Large Imaging System (ALIS) there is need of stable methods for analysis and classification of auroral images and images with for example mother of pearl clouds. This part of ALIS is called Selective Imaging Techniques (SIT) and is intended to sort out images of scientific interest. It's also used to find out what and where in the images there is for example different auroral phenomena's. We will discuss some about the SIT units main functionality but this work is mainly concentrated on how to find auroral arcs and how they are placed in images. Special case have been taken to make the algorithm robust since it's going to be implemented in a SIT unit which will work automatic and often unsupervised and some extends control the data taking of ALIS. The method for finding auroral arcs is based on a local operator that detects intensity differens. This gives arc orientation values as a preprocessing which is fed to a neural network classifier. We will show some preliminary results and possibilities to use and improve this algorithm for use in the future SIT unit.

Paper Details

Date Published: 22 March 1999
PDF: 6 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343029
Show Author Affiliations
Peter Rydesater, Mid Sweden Univ. (Sweden)
Urban Brandstrom, Swedish Institute of Space Physics (Sweden)
Ake Steen, Swedish Institute of Space Physics (Sweden)
Bjorn Gustavsson, Swedish Institute of Space Physics (Sweden)

Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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