Coronal mass ejection detection method based on improved U-Net network

Abstract

The invention discloses a coronal mass ejection detection method based on an improved U-Net network, and the method comprises the following steps: 1, carrying out image preprocessing of an LASCO C2 image downloaded from an SOHO homepage, wherein the preprocessing comprises image graying, image rotating and overturning; 2, converting the LASCO C2 image obtained in the step 1 into a polar coordinatefor representation; 3, making a corona image data set, and dividing the data set into a training set and a test set; 4, improving an original UNet network to enable the UNet network to adapt to a coronal mass ejection detection task; and 5, conducting training by using the improved UNet network, and finely adjusting network parameters to finally obtain a detection result. Automatic detection of acoronal mass ejection phenomenon is achieved, the difficulty of manual recording is reduced, and the automatic detection speed is increased.

Description

technical field [0001] The invention relates to the field of astronomical image target detection, in particular to a detection method for coronal mass ejection based on an improved U-Net network. Background technique [0002] Coronal mass ejections (Coronal Mass Ejections, CMEs) are frequent eruptions in the solar atmosphere, and their eruption time and frequency vary with the frequency of solar activity, specifically manifested in the coronal structure within a time interval of several minutes to several hours. Significantly altered with observable ejection of matter, usually manifested as a bright, intricately textured enhanced structure, often accompanied by a dark region of insufficient brightness in its tail. A coronal mass ejection has not been clearly defined, the earlier definition being a visible change in the structure of the corona, including the appearance and outward motion of a new, discrete, bright white light feature in the coronal view. Coronal mass ejectio...

AI Technical Summary

Problems solved by technology

[0006] The above methods have more or less certain problems. For example, the methods based on grayscale features, such as CACTus and SEEDS, all use brightness enhancement to highlight the suspected CMEs area in the coronal image, and enhance the target area of ​​CMEs. The intensity of the CMEs area, and the detection results will be greatly affected by the detection rules and...

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