A New Square Grid-Based Method for Identifying Solar Activities

Abstract

The FITS is widely used as a common file format in astronomy. However, how to detect an effective celestial event from large-scale astronomical images with complex background is a thorny issue. This paper proposes a square grid structure for target detection (GSTD) of solar activities, inncluding the separation of the target area and the deletion of the background area. This kind of processing can greatly reduce the storage cost of the data set, while the solar activity area can be well preserved. The solar image is operated through an equally sized square grid to separate the solar activity target area and the background area to speed up the processing of images, improve processing accuracy, and effectively prevent image noise interference. The experiment results have shown that this method delivers satisfactory performance in accuracy and time-cost. Through the anti-jamming processing of image noise, the accurate positioning and effective recognition of solar activities are realized. This method has been proved to achieve good image segmentation recognition of solar phenomenon in the research of various types of solar activities. Moreover, it can provide a feasible way to reduce the storage occupancy of Content-Based Image Retrieval (CBIR) systems.