Automatic detection of sunspots on solar continuum HMI images blending local–global threshold

Abstract

A huge collection of solar images to visualize sunspot are acquired by various solar observatories spread across the globe. This necessitates efficient tools for detecting and analyzing the sunspots encompassing diverse solar features. One such contribution is delivered in this work by exploiting the intrinsic intensity variations of solar images associated with sunspots and their attributes. The presented mechanism initially, pre-processes the acquired solar images by correcting the intensity variations introduced while profiling from the sun center to the limb followed by smoothening using a localized window. The resultant is then differenced from the global threshold that is obtained as a result of the statistical analysis computed over the probability distribution function of the input image. This arrangement offers higher discerning variations concerned with the local contextual structures related to sunspot, umbra, and penumbra. Also, it captures the major gradient variation between these regions that adds to the pixel heterogeneity surrounding them to finally render an automatic sunspot detection mechanism distinguishing the diverse solar regions. Receiver Operating Characteristics (ROC) investigation on annual solar images in Flexible Image Transport System (FITS) format reveals the presented method’s efficacy. Also, Pearson correlation analysis of the evaluated sunspot numbers from the detected sunspots with the solar catalog reveals the scheme’s detection closeness. Moreover, the model’s simplicity analyzed along the time and space dimensions affirms its extension to real-time analysis