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

Automated Coronal Hole Identification via Multi-Thermal Intensity Segmentation

Coronal holes (CH) are regions of open magnetic fields that appear as dark areas in the solar corona due to their low density and temperature compared to the surrounding quiet corona. To date, accurate identification and segmentation of CHs has been a difficult task due to their comparable intensity to local quiet Sun regions. Current segmentation methods typically rely on the use of single EUV passband and magnetogram images to extract CH information. Here, the Coronal Hole Identification via Multi-thermal Emission Recognition Algorithm (CHIMERA) is described, which analyses multi-thermal images from the Atmospheric Image Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) to segment coronal hole boundaries by their intensity ratio across three passbands (171 \AA, 193 \AA, and 211 \AA). The algorithm allows accurate extraction of CH boundaries and many of their properties, such as area, position, latitudinal and longitudinal width, and magnetic polarity of segmented CHs. From these properties, a clear linear relationship was identified between the duration of geomagnetic storms and coronal hole areas. CHIMERA can therefore form the basis of more accurate forecasting of the start and duration of geomagnetic storms

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

Solar stereoscopy - where are we and what developments do we require to progress?

Observations from the two STEREO-spacecraft give us for the first time the possibility to use stereoscopic methods to reconstruct the 3D solar corona. Classical stereoscopy works best for solid objects with clear edges. Consequently an application of classical stereoscopic methods to the faint structures visible in the optically thin coronal plasma is by no means straight forward and several problems have to be treated adequately: 1.)First there is the problem of identifying one dimensional structures -e.g. active region coronal loops or polar plumes- from the two individual EUV-images observed with STEREO/EUVI. 2.) As a next step one has the association problem to find corresponding structures in both images. 3.) Within the reconstruction problem stereoscopic methods are used to compute the 3D-geometry of the identified structures. Without any prior assumptions, e.g., regarding the footpoints of coronal loops, the reconstruction problem has not one unique solution. 4.) One has to estimate the reconstruction error or accuracy of the reconstructed 3D-structure, which depends on the accuracy of the identified structures in 2D, the separation angle between the spacecraft, but also on the location, e.g., for east-west directed coronal loops the reconstruction error is highest close to the loop top. 5.) Eventually we are not only interested in the 3D-geometry of loops or plumes, but also in physical parameters like density, temperature, plasma flow, magnetic field strength etc. Helpful for treating some of these problems are coronal magnetic field models extrapolated from photospheric measurements, because observed EUV-loops outline the magnetic field. This feature has been used for a new method dubbed 'magnetic stereoscopy'. As examples we show recent application to active region loops.Comment: 12 Pages, 9 Figures, a Review articl

Solar rotation speed by detecting and tracking of Coronal Bright Points

Coronal Bright Points are one of many Solar manifestations that provide scientists evi-dences of its activity and are usually recognized by being small light dots, like scattered jewels. For many years these Bright Points have been overlooked due to another element of solar activity, sunspots, which drawn scientists full attention mainly because they were easier to detect. Never-theless, CBPs as a result of a clear distribution across all latitudes, provide better tracers to study Solar corona rotation. A literature review on CBPs detection and tracking unveiled limitations both in detection accuracy and lacking an automated image processing feature. The purpose of this dissertation was to present an alternative method for detecting CBPs using advanced image processing techniques and provide an automatic recognition software. The proposed methodology is divided into pre-processing methods, a segmentation section, post processing and a data evaluation approach to increase the CBP detection efficiency. As iden-tified by the study of the available data, pre-processing transformations were needed to ensure each image met certain specifications for future detection. The detection process includes a gra-dient based segmentation algorithm, previously developed for retinal image analysis, which is now successfully applied to this CBP case study. The outcome is the CBP list obtained by the detection algorithm which is then filtered and evaluated to remove false positives. To validate the proposed methodology, CBPs need to be tracked along time, to obtain the rotation of the Solar corona. Therefore, the images used in this study were taken from 19.3nm wavelength by the AIA 193 instrument on board of the Solar Dynamics Observatory (SDO) sat-ellite over 3 days during august 2010. These images allowed the perception of how CBPs angular rotation velocity not only depends on heliographic latitude, but also on other factors such as time. From the results obtained it was clear that the proposed methodology is an effective method to detect and track CBPs providing a consistent method for its detection