Solar activity target detection method based on square grid structure

Abstract

The invention discloses a solar activity target detection method based on a square grid structure, and belongs to the technical field of astronomy and image processing. The solar activity target detection method comprises the following steps that image normalization is performed; the gray scale of an original image is reduced; the square grid structure is defined and the image is divided; a rectangular coordinate system for storing the grid images is established, and dimension reduction and storage are performed on grid units; an initial threshold is estimated by utilizing a histogram threshold method; the minimum external rectangle is used as a tool to correct the threshold so that gradient thresholds are obtained; a target area is identified and a background area is hidden; and segmentation and saving of the target area are performed. Compared with the methods in the prior art, image segmentation of multiple types of solar activities is realized, and a feasible method for solving the problem of storing massive astronomical data is also provided.

Description

technical field [0001] The invention relates to a method for detecting solar moving targets based on a square grid structure, and belongs to the fields of astronomical technology and image processing technology. Background technique [0002] In the field of astronomical research, the FITS format is generally used to store astronomical images. As the quality of ingested astronomical images is getting higher and higher, large-scale storage of astronomical images is becoming a thorny problem. Many existing image segmentation methods still do not solve the above problems, because the segmentation results still retain irrelevant background areas, and the size of the data set does not change significantly, which will increase the time and space overhead of later feature extraction. [0003] There are several methods to identify solar activity on a single image, such as seed region growing method, watershed algorithm and threshold method. Although, the current identification algo...

AI Technical Summary

Problems solved by technology

However, solar activities such as coronal ejection and dark stripe activation have relatively inconspicuous grayscale features. The segmentation of the above solar activities by the region growing method and the watershed method will cause two extreme results: over-segmentation or image voids.

Currently there is no general method for identifying multiple solar events on full-helioscopic images

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