New shape-based auroral oval segmentation driven by LLS-RHT

A new method that exploits shape to localize the auroral oval in satellite imagery is introduced. The core of the method is driven by the linear least-squares (LLS) randomized Hough transform (RHT). The LLS-RHT is a new fast variant of the RHT suitable when not all necessary conditions of the RHT can be satisfied. The method is also compared with the three existing methods for aurora localization, namely the histogram-based k-means [C.C. Hung, G. Germany, K-means and iterative selection algorithms in image segmentation, IEEE Southeastcon 2003 (Session 1: Software Development)], adaptive thresholding [X. Li, R. Ramachandran, M. He, S. Movva, J.A. Rushing, S.J. Graves, W. Lyatsky, A. Tan, G.A. Germany, Comparing different thresholding algorithms for segmenting auroras, in: Proceedings of the International Conference on Information Technology: Coding and Computing, vol. 6, 2004, pp. 594–601], and pulse-coupled neural network-based [G.A. Germany, G.K. Parks, H. Ranganath, R. Elsen, P.G. Richards, W. Swift, J.F. Spann, M. Brittnacher, Analysis of auroral morphology: substorm precursor and onset on January 10, 1997, Geophys. Res. Lett. 25 (15) (1998) 3042–3046] methods. The methodologies and their performance on real image data are both considered in the comparison. These images include complications such as random noise, low contrast, and moderate levels of key obscuring phenomena.