Abstract
A fast hybrid system for the automated detection and verification of active regions (plages) and filaments in solar images is presented in this paper. The system combines automated image processing with machine learning. The imaging part consists of five major stages. The solar disk is detected in the first stage, using a morphological hitโmiss transform, watershed transform and Filling algorithm. An imageโenhancement technique is introduced to remove the limbโdarkening effect and intensity filtering is implemented followed by a modified regionโgrowing technique to detect the regions of interest (RoI). The algorithms are tested on Hโฮฑ and CA II K3โline solar images that are obtained from Meudon Observatory, covering the period from July 2, 2001 till August 4, 2001. The detection algorithm is fast and it achieves false acceptance rate (FAR) error rate of 67% and false rejection rate (FRR) error rate of 3% for active regions, and FAR error rate of 19% and FRR error rate of 14% for filaments, when compared with the manually detected filaments in the synoptic maps. The detection performance is enhanced further using a neural network (NN), which is trained on statistical features extracted from the RoI and nonโRoI. With the use of this combination the FAR has dropped to 2% for active regions and 4% for filaments.ยฉ 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 199โ210, 2005