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Feature Extraction For Classification Of Auroral Images, Shwetha Herga

Theses

Auroras are a dynamically evolving phenomenon. Different auroral forms are correlated with various physical processes in the magnetosphere and ionosphere system. Millions of auroral images are captured every year by the modern ground-based All-Sky Imager(ASI). In dealing with data from ASI, machine learning techniques play a critical scientific role, facilitating both efficient searches and statistical studies. In this work, we manually label night-side auroral images from various Time History of Events and Macroscale Interactions during Substorms (THEMIS) all-sky imager based on the sky conditions; the labels are clear sky with auroras, cloudy with the moon, cloudy, clear-sky with the moon, …