Towards feature selection for appearance models in solar event tracking

Abstract

Classification of solar event detections into two classes, of either the same object at a later time or an entirely different object, plays a significant role in multiple hypothesis solar event tracking. Many features for this task are produced when images from multiple wavelengths are used and compounded when multiple image parameters are extracted from each of these observations coming from NASA’s Solar Dynamics Observatory. Furthermore, each different event type may require different sets of features to accurately accomplish this task. A feature selection algorithm is required to identify important features extracted from the available images and that can do so without a high computational cost. This work investigates the use of a simple feature subset selection method based on the ANOVA F-Statistic measure as a means of ranking the extracted image parameters in various wavelengths. We show that the feature subsets that are obtained through selecting the top K features ranked in this manner produce classification results as good or better than more complicated methods based on searching the feature subset space for maximum-relevance and minimum-redundancy. We intend for the results of this work to lay the foundations of future work towards a robust model of appearance to be used in the tracking of solar phenomena.