Diurnal auroral occurrence statistics obtained via machine vision
Modern ground-based digital auroral All-Sky Imager (ASI) networks capture millions of images annually. Machine vision techniques are widely utilised in the retrieval of images from large data bases. Clearly, they can play an important scientific role in dealing with data from auroral ASI network...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2004-04-01
|
| Series: | Annales Geophysicae |
| Online Access: | https://www.ann-geophys.net/22/1103/2004/angeo-22-1103-2004.pdf |
| Summary: | Modern ground-based digital auroral All-Sky Imager (ASI) networks
capture millions of images annually. Machine vision techniques are
widely utilised in the retrieval of images from large data
bases. Clearly, they can play an important scientific role in dealing
with data from auroral ASI networks, facilitating both efficient
searches and statistical studies. Furthermore, the development of
automated techniques for identifying specific types of aurora opens up
the potential of ASI control software that would change instrument
operation in response to evolving geophysical conditions. In this
paper, we describe machine vision techniques that we have developed
for use on large auroral image data sets. We present the results of
application of these techniques to a 350000 image subset of the CANOPUS Gillam ASI in
the years 1993–1998. In particular, we obtain occurrence statistics
for auroral arcs, patches, and Omega-bands. These results agree with
those of previous manual auroral surveys.<br><br><b>Key words.</b> Ionosphere (Instruments and techniques) General
(new fields) |
|---|---|
| ISSN: | 0992-7689 1432-0576 |