Aurora Image Detection Based On Global And Texture Features

Aurora is a colorful,varied,unpredictable luminous phenomenon,it appears in the high magnetic latitude area of the planet,and generally only in the north and south poles of the high latitude areas.The earth's aurora,which is generated by the high-level atmospheric molecules,atomic ionization or excitation,is the sun's high-energy particle flow and the results of the Earth's magnetosphere.Thus it can be seen,the atmosphere,high-energy particles and magnetic field is a necessary condition for appearance of the aurora.The study of the Earth's aurora helps us to understand the impact of the Earth's activities by the sun,which helps us to grasp the process of weather changes well.In addition to the Earth,the aurora still exists in the solar system in some of the magnetosphere.Therefore,the study of the Earth's aurora will also help us to further understand the other planets in the solar system,which lays the foundation for the human to solve more mysteries of the universe.It also has very important value and significance for scientific research.The Detection of the aurora in the image is the foundation of the research of the aurora morphological recognition.So far,it is a common way to use the pattern recognition technology to determine whether the image is an aurora image or not.From the perspective of learning methods,the steps of the aurora image detection are listed above:first extract the image features,and then calculate the degree of similarity between images,finally classified them.In this process,the step of feature extraction is essential,it will directly affect the follow-up classification.The most commonly used method is the local binary mode operator,abbreviated as LBP.Previously,researchers have proposed a feature extraction method based on digital image feature,including simple strength,texture and brightness of these characteristics.However,these features can not achieve a good description of the effect of the image compared to LBP.For this reason,LBP has been widely used in this area.With the deepening of the study of the auroral images,the shortcomings of this method become increasingly obvious,the basic LBP operator is more sensitive to noise,and once the image rotate,the value of the operator will change.For its own shortcomings,A robust feature extraction method,which is called the Zernike moment feature,is proposed in this paper.The Zernike moment transforms the image from the Cartesian coordinates to the polar coordinates,and the modulus is unchanged after the rotation.Experiments show that the method proposed in this paper is much better than that of LBP when the test set image is mixecd with noise,rotated by an angle or classified before filtering.Form this view,the Zemike moments feature extraction method overcomes many of the shortcomings of LBP.In addition,it is time-consuming to detect the auroral image compared with LBP.This paper also presents a method of auroral image detection based on Gabor feature.The experiment shows that this method runs faster than LBP.Except this,if the test set of the images are filtered before classification,The accuracy of classification of auroral detection algorithm based on Gabor feature is better than that of LBP.