Automatic detection of limb prominences in 304 A EUV images

A new algorithm for automatic detection of prominences on the solar limb in 304 A EUV images is presented, and results of its application to SOHO/EIT data discussed. The detection is based on the method of moments combined with a classifier analysis aimed at discriminating between limb prominences, active regions, and the quiet corona. This classifier analysis is based on a Support Vector Machine (SVM). Using a set of 12 moments of the radial intensity profiles, the algorithm performs well in discriminating between the above three categories of limb structures, with a misclassification rate of 7%. Pixels detected as belonging to a prominence are then used as starting point to reconstruct the whole prominence by morphological image processing techniques. It is planned that a catalogue of limb prominences identified in SOHO and STEREO data using this method will be made publicly available to the scientific community

Solar activity detection and prediction using image processing and machine learning techniques

The objective of the research in this dissertation is to develop the methods for automatic detection and prediction of solar activities, including prominence eruptions, emerging flux regions and solar flares. Image processing and machine learning techniques are applied in this study. These methods can be used for automatic observation of solar activities and prediction of space weather that may have great influence on the near earth environment. The research presented in this dissertation covers the following topics: i) automatic detection of prominence eruptions (PBs), ii) automatic detection of emerging flux regions (EFRs), and iii) automatic prediction of solar flares. In detection of prominence eruptions, an automated method is developed by combining image processing and pattern recognition techniques. Consecutive Hu solar images are used as the input. The image processing techniques, including image transformation, segmentation and morphological operations are used to extract the limb objects and measure the associated properties. The pattern recognition techniques, such as Support Vector Machine (SVM), are applied to classify all the objects and generate a list of identified the PBs as the output. In detection of emerging flux regions, an automatic detection method is developed by using multi-scale circular harmonic filters, Kalman filter and SVM. The method takes a sequence of consecutive Michelson Doppler Imager (MDI) magnetograms as the input. The multi-scale circular harmonic filters are applied to detect bipolar regions from the solar disk surface and these regions are traced by Kalman filter until their disappearance. Finally, a SVM classifier is applied to distinguish EFRs from the other regions based on statistical properties. In solar flare prediction, it is modeled as a conditional density estimation (CDE) problem. A novel method is proposed to solve the CDE problem using kernel-based nonlinear regression and moment-based density function reconstruction techniques. This method involves two main steps. In the first step, kernel-based nonlinear regression techniques are applied to predict the conditional moments of the target variable, such as flare peak intensity or flare index. In the second step, the condition density function is reconstructed based on the estimated moments. The method is compared with the traditional double-kernel density estimator, and the experimental results show that it yields the comparable performance of the double-kernel density estimator. The most important merit of this new method is that it can handle high dimensional data effectively, while the double-kernel density estimator has confined to the bivariate case due to the difficulty of determining optimal bandwidths. The method can be used to predict the conditional density function of either flare peak intensity or flare index, which shows that our method is of practical significance in automated flare forecasting

Investigating the Kinematics of Coronal Mass Ejections with the Automated CORIMP Catalog

Studying coronal mass ejections (CMEs) in coronagraph data can be challenging due to their diffuse structure and transient nature, compounded by the variations in their dynamics, morphology, and frequency of occurrence. The large amounts of data available from missions like the Solar and Heliospheric Observatory (SOHO) make manual cataloging of CMEs tedious and prone to human error, and so a robust method of detection and analysis is required and often preferred. A new coronal image processing catalog called CORIMP has been developed in an effort to achieve this, through the implementation of a dynamic background separation technique and multiscale edge detection. These algorithms together isolate and characterise CME structure in the field-of-view of the Large Angle Spectrometric Coronagraph (LASCO) onboard SOHO. CORIMP also applies a Savitzky-Golay filter, along with quadratic and linear fits, to the height-time measurements for better revealing the true CME speed and acceleration profiles across the plane-of-sky. Here we present a sample of new results from the CORIMP CME catalog, and directly compare them with the other automated catalogs of Computer Aided CME Tracking (CACTus) and Solar Eruptive Events Detection System (SEEDS), as well as the manual CME catalog at the Coordinated Data Analysis Workshop (CDAW) Data Center and a previously published study of the sample events. We further investigate a form of unsupervised machine learning by using a k-means clustering algorithm to distinguish detections of multiple CMEs that occur close together in space and time. While challenges still exist, this investigation and comparison of results demonstrates the reliability and robustness of the CORIMP catalog, proving its effectiveness at detecting and tracking CMEs throughout the LASCO dataset.Comment: 23 pages, 11 figures, 1 tabl

Framework for near real time feature detection from the atmospheric imaging assembly images of the solar dynamics observatory

The study of the variability of the solar corona and the monitoring of its traditional regions (Coronal Holes, Quiet Sun and Active Regions) are of great importance in astrophysics as well as in view of the Space Weather applications. The Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) provides high resolution images of the sun imaged at different wavelengths at a rate of approximately one every 10 seconds, a great resource for solar monitoring . Today, the process of identifying features and estimating their properties is applied manually in an iterative fashion to verify the detection results. We introduce a complete, automated image-processing pipeline, starting with raw data and ending with quantitative data of high level feature parameters. We implement two multichannel unsupervised algorithms that automatically segments EUV AIA solar images into Coronal Holes, Quiet Sun and Active Regions in near real time. We also develop a method of post processing to deal with fragments in a segmented image by spatial validity based compact clustering. The segmentation results are consistent with well-known algorithms and databases. The parameters extracted from the segments like area closely follow the solar activity pattern. Moreover, the methods developed within the proposed framework are generic enough to allow the study of any solar feature (e.g. Coronal Bright points) provided that the feature can be deduced from AIA images

Image enhancement techniques applied to solar feature detection

This dissertation presents the development of automatic image enhancement techniques for solar feature detection. The new method allows for detection and tracking of the evolution of filaments in solar images. Series of H-alpha full-disk images are taken in regular time intervals to observe the changes of the solar disk features. In each picture, the solar chromosphere filaments are identified for further evolution examination. The initial preprocessing step involves local thresholding to convert grayscale images into black-and-white pictures with chromosphere granularity enhanced. An alternative preprocessing method, based on image normalization and global thresholding is presented. The next step employs morphological closing operations with multi-directional linear structuring elements to extract elongated shapes in the image. After logical union of directional filtering results, the remaining noise is removed from the final outcome using morphological dilation and erosion with a circular structuring element. Experimental results show that the developed techniques can achieve excellent results in detecting large filaments and good detection rates for small filaments. The final chapter discusses proposed directions of the future research and applications to other areas of solar image processing, in particular to detection of solar flares, plages and sunspots