Coronal mass ejection (CME) detection method based on multi-feature fusion

Abstract

The invention discloses a coronal mass ejection (CME) detection method based on multi-feature fusion. Through segmenting a CME differential image, CME detection is modeled to be the classification problem of a brightest block in the differential image. The method comprises the following steps: firstly, an original image is converted into polar coordinates for display; secondly, since the typical representation of CME is a bright and complex-texture enhancement structure, the brightest block of the CME is searched for, the brightest block is taken as a representative of the image, the gray scale, texture and HOG features of the brightest block are extracted; and finally, by taking the extracted gray-scale feature, the texture and the HOG feature as a basis, taking a decision tree as a basic classifier, and finishing CME detection by use of an integrated decision tree. An experiment result shows that the CME detection algorithm based on the multi-feature fusion, brought forward by the invention, can obtain a quite good CME detection result.

Description

technical field [0001] The invention relates to a detection method for coronal mass ejection based on multi-feature fusion. Background technique [0002] Coronal mass ejections (Coronal Mass Ejections, CMEs) is a frequent eruption phenomenon in the solar atmosphere, which is characterized by obvious changes in the structure of the corona on a time scale of minutes to hours, accompanied by observable mass ejections, usually manifested as a A bright, complexly textured enhanced structure, followed by a dark region of insufficient brightness. CME is not only a transient phenomenon, it may also play an important role in the long-term evolution of the corona; at the same time, it has a close relationship with many interplanetary disturbances, and can cause drastic changes in the Earth's space environment; and the intensity of CME, Angles can have a significant impact on space weather, so the quantitative study of CME is of great significance to the disciplines of solar physics a...

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Problems solved by technology

However, it uses the number of bright spots as the grayscale statistical feature, ignoring the texture information of the CME structure; on the other hand, it is difficult to describe the radial propagation mode of CME by using the rectangular block cutting method

[0006] In short, the existing research in the field of CME detection often relies on the detection methods of single features such as grayscale and texture, and most of them use traditional digital image processing technology for detection, so it is difficult to obtain good detection results

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