An Automatic Segmentation Algorithm for Solar Filaments in H-Alpha Images using a Context-based Sliding Window

Abstract

There are many features which appear on the surface of the sun. One of these features that appear clearly are the dark threads in the Hydrogen alpha (Hα) spectrum solar images. These 'filaments' are found to have a definite correlation with Coronal Mass Ejections (CMEs). A CME is a large release of plasma into space. It can be hazardous to astronauts and the spacecraft if it is being ejected towards the Earth. Knowing the exact attributes of solar filaments may open the way towards predicting the occurrence of CMEs. In this paper, an efficient and fully automated algorithm for solar filament segmentation without compromising accuracy is proposed. The algorithm uses some statistical measures to design the thresholding equations and it is written in the C++ programming language. The square root of the range as a measure of variability of image intensity values is used to determine the size of the sliding window at run time. There are many previous studies in this area, but no single segmentation method that could precisely claim to be fully automatic exists. Samples were taken from several representative regions in low-contrast and high-contrast solar images to verify the viability and efficacy of the method.

Solar Filament Segmentation Based on Improved U-Nets

“…Finally, the sunspots were eliminated according to the difference in size and aspect ratio, which greatly improved the precision of segmentation. Atoum (2018) proposed an adaptive-sized window to detect filaments. The experiment first used a fixed-size sliding window to traverse the image, determined the size of window two according to the absolute difference of pixels in the original window, then the mean and standard deviation of pixel intensity in window two were used to determine whether the pixel was a filament.…”