Automatic segmentation of the fine structures of sunspots in high-resolution solar images

Context. With the development of large-aperture ground-based solar telescopes and the adaptive optics system, the resolution of the obtained solar images has become increasingly higher. In the high-resolution photospheric images, the fine structures (umbra, penumbra, and light bridge) of sunspots can be observed clearly. The research of the fine structures of sunspots can help us to understand the evolution of solar magnetic fields and to predict eruption phenomena that have significant impacts on the Earth, such as solar flares. Therefore, algorithms for automatically segmenting the fine structures of sunspots in high-resolution solar image will greatly facilitate the study of solar physics. Aims. This study is aimed at proposing an automatic fine-structure segmentation method for sunspots that is accurate and requires little time. Methods. We used the superpixel segmentation to preprocess a solar image. Next, the intensity information, texture information, and spatial location information were used as features. Based on these features, the Gaussian mixture model was used to cluster different superpixels. According to different intensity levels of the umbra, penumbra, and quiet photosphere, the clusters were classified into umbra, penumbra, and quiet-photosphere areas. Finally, the morphological method was used to extract the light-bridge area. Results. The experimental results show that the method we propose can segment the fine structures of sunspots quickly and accurately. In addition, the method can process high-resolution solar images from different solar telescopes and generates a satisfactory segmentation performance