Diurnal auroral occurrence statistics obtained via machine vision

Syrjäsuo, M.; Donovan, E.

doi:10.5194/angeo-22-1103-2004

Cited by 67 publications

(63 citation statements)

References 29 publications

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“…The ionospheric electrodynamic characteristics of omega bands have been described in several papers (see, e.g., Wild et al 2000Wild et al , 2011Syrjäsuo and Donovan 2004;Amm et al 2005;Vanhamäki et al 2009, and references therein), all of which support the overall picture of a sequence of upward and downward field-aligned currents (FACs) located in the bright and dark regions, respectively, of such structures. As the band structure with its temporally stationary current system moves above ground-based magnetometers, Ps6-type magnetic pulsations (with periods of 5-40 min) are observed (Saito 1978;Rostoker and Barichello 1980).…”

Section: Introductionmentioning

confidence: 93%

Morphologies of omega band auroras

Sato

Yukimatu

Tanaka

et al. 2017

Earth Planets Space

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We examined the morphological signatures of 315 omega band aurora events observed using the Time History of Events and Macroscale Interactions during Substorm ground-based all-sky imager network over a period of 8 years. We find that omega bands can be classified into the following three subtypes: (1) classical (O-type) omega bands, (2) torch or tongue (T-type) omega bands, and (3) combinations of classical and torch or tongue (O/T-type) omega bands. The statistical results show that T-type bands occur the most frequently (45%), followed by O/T-type bands (35%) and O-type bands (18%). We also examined the morphologies of the omega bands during their formation, from the growth period to the declining period through the maximum period. Interestingly, the omega bands are not stable, but rather exhibit dynamic changes in shape, intensity, and motion. They grow from small-scale bumps (seeds) at the poleward boundary of preexisting east-west-aligned auroras, rather than via the rotation or shear motion of preexisting east-west-aligned auroras, and do not exhibit any shear motion during the periods of auroral activity growth. Furthermore, the auroral luminosity is observed to increase during the declining period, and the total time from the start of the growth period to the end of the declining period is found to be about 20 min. Such dynamical signatures may be important in determining the mechanism responsible for omega band formation.

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Section: Introductionmentioning

confidence: 93%

Morphologies of omega band auroras

Sato

Yukimatu

Tanaka

et al. 2017

Earth Planets Space

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“…So there is slight tendency for smaller structures to be less accelerated. However, no clear division point or clear relation between energies and auroral arcs as a function of MLT (Syrjäsuo and Donovan, 2004). The arc occurrence curve, too, has been normalised by the total amount of ASC images taken in each MLT sector.…”

Section: Peak Energy Arc Widths and Kpmentioning

confidence: 99%

Statistical study of inverted-V structures in FAST data

2008

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Abstract. Since the 1970s the inverted-V structures have been observed in the satellite-borne particle data. Theseshaped enhancements of the differential energy flux on the high energy channels on the top of low number flux on the lower energies are traces of auroral particle acceleration by U-shaped potential drops along the field lines within the auroral zones. In this study, we show the results of a statistical study of inverted-V events in five years (1997)(1998)(1999)(2000)(2001) of the Fast Auroral SnapshoT (FAST) electron spectrograms. We found 588 well-defined events in total, but the statistical results are also shown for a subset of 97 very best events.The statistical analysis shows that the inverted-V's mainly occur within the auroral oval latitudes (65 • -75 • invariant latitude), in the pre-midnight sector (21-23 magnetic local time) and during the Northern Hemisphere winter months (November to February). The typical maximum energies of the inverted-V's are 2-4 keV, when a threshold value of 0.3 keV is used as a selection criterion. This corresponds to an acceleration potential drop of a few kV. The typical peak energy tends to increase towards the magnetic midnight. Most of the inverted-V events occur during magnetically quiet or moderate activity, K p 1-4. The usual width of an inverted-V varies from 20 to 40 km and is comparable to the peak value of the typical meso-scale auroral arc width of 10-20 km. Furthermore, the polar cap potential values estimated from the SuperDARN radar data are always at least three times larger than the inverted-V acceleration potential drop.

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“…Aurora classification has important research significance, and it has attracted the interest of researchers in the field of computer vision. In 2004, Syrjäsuo et al [6] first introduced computer vision into aurora image classification. They classified auroras into arcs, patchy auroras, omega bands, and north-south structures according to the shape, and used Fourier transformation to extract the features of the aurora images.…”

Section: Introductionmentioning

confidence: 99%

“…Fu et al [9] combined morphological analysis (MCA) with aurora image analysis to separate the aurora texture region from the background region and preserve the aurora texture features, which is a method that can Aurora classification has important research significance, and it has attracted the interest of researchers in the field of computer vision. In 2004, Syrjäsuo et al [6] first introduced computer vision into aurora image classification. They classified auroras into arcs, patchy auroras, omega bands, and north-south structures according to the shape, and used Fourier transformation to extract the features of the aurora images.…”

Section: Introductionmentioning

confidence: 99%

Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation

et al. 2018

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Due to the rich physical meaning of aurora morphology, the classification of aurora images is an important task for polar scientific expeditions. However, the traditional classification methods do not make full use of the different features of aurora images, and the dimension of the description features is usually so high that it reduces the efficiency. In this paper, through combining multiple features extracted from aurora images, an aurora image classification method based on multi-feature latent Dirichlet allocation (AI-MFLDA) is proposed. Different types of features, whether local or global, discrete or continuous, can be integrated after being transformed to one-dimensional (1-D) histograms, and the dimension of the description features can be reduced due to using only a few topics to represent the aurora images. In the experiments, according to the classification system provided by the Polar Research Institute of China, a four-class aurora image dataset was tested and three types of features (MeanStd, scale-invariant feature transform (SIFT), and shape-based invariant texture index (SITI)) were utilized. The experimental results showed that, compared to the traditional methods, the proposed AI-MFLDA is able to achieve a better performance with 98.2% average classification accuracy while maintaining a low feature dimension.

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Diurnal auroral occurrence statistics obtained via machine vision

Cited by 67 publications

References 29 publications

Morphologies of omega band auroras

Morphologies of omega band auroras

Statistical study of inverted-V structures in FAST data

Aurora Image Classification Based on Multi-Feature Latent Dirichlet Allocation

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