DCT-Tensor-Net for Solar Flares Detection on IRIS Data

Flares are an eruptive phenomenon observed on the sun, which are major protagonists in space weather and can cause adverse effects such as disruptions in communication, power grid failure and damage of satellites. Our method answers the importance of the time component in some scientific video obser...

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Bibliographic Details
Main Authors: Ullmann, Denis, Voloshynovskiy, Slava, Kleint, Lucia, Krucker, Sam, Melchior, Martin, Huwyler, Cedric, Panos, Brandon
Format: Conference Proceeding
Language:English
Subjects:
astrophysics
big data applications
computer aided analysis
data analysis
data preprocessing
data processing
detection algorithm
Discrete cosine transforms
feature extraction
Frequency-domain analysis
image texture analysis
Iris
Satellites
scientific computing
Sun
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Summary:Flares are an eruptive phenomenon observed on the sun, which are major protagonists in space weather and can cause adverse effects such as disruptions in communication, power grid failure and damage of satellites. Our method answers the importance of the time component in some scientific video observations, especially for flare detection and the study is based on NASA's Interface Region Imaging Spectrograph (IRIS) observations of the sun since 2013, which consists of a very asymmetrical and unlabeled big data. For detecting and analyzing flares in our IRIS solar video observation data, we created a discrete cosine transform tool DCT- Tensor-Net which uses an empirically handcrafted harmonic representation of our video data. This is one of the first tools for detecting flares based on IRIS images. Our method reduces the false detections of flares by taking into consideration their specific local spatial and temporal patterns.
ISSN:2471-8963
DOI:10.1109/EUVIP.2018.8611672