Automatic Short-Term Solar Flare Prediction Using Machine Learning and Sunspot Associations.

YesIn this paper, a machine-learning-based system that could provide automated short-term solar flare prediction is presented. This system accepts two sets of inputs: McIntosh classification of sunspot groups and solar cycle data. In order to establish a correlation between solar flares and sunspot groups, the system explores the publicly available solar catalogues from the National Geophysical Data Center to associate sunspots with their corresponding flares based on their timing and NOAA numbers. The McIntosh classification for every relevant sunspot is extracted and converted to a numerical format that is suitable for machine learning algorithms. Using this system we aim to predict whether a certain sunspot class at a certain time is likely to produce a significant flare within six hours time and if so whether this flare is going to be an X or M flare. Machine learning algorithms such as Cascade-Correlation Neural Networks (CCNNs), Support Vector Machines (SVMs) and Radial Basis Function Networks (RBFN) are optimised and then compared to determine the learning algorithm that would provide the best prediction performance. It is concluded that SVMs provide the best performance for predicting whether a McIntosh classified sunspot group is going to flare or not but CCNNs are more capable of predicting the class of the flare to erupt. A hybrid system that combines a SVM and a CCNN is suggested for future use.EPSR

Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images

yesThis paper presents a hybrid system for automatic detection and McIntosh-based classification of sunspot groups on SOHO/MDI white-light images using active-region data extracted from SOHO/MDI magnetogram images. After sunspots are detected from MDI white-light images they are grouped/clustered using MDI magnetogram images. By integrating image-processing and neural network techniques, detected sunspot regions are classified automatically according to the McIntosh classification system. Our results show that the automated grouping and classification of sunspots is possible with a high success rate when compared to the existing manually created catalogues. In addition, our system can detect and classify sunspot groups in their early stages, which are usually missed by human observers.EPSR

Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares

yesThe importance of real-time processing of solar data especially for space weather applications is increasing continuously. In this paper, we present an automated hybrid computer platform for the short-term prediction of significant solar flares using SOHO/Michelson Doppler Imager images. This platform is called the Automated Solar Activity Prediction tool (ASAP). This system integrates image processing and machine learning to deliver these predictions. A machine learning-based system is designed to analyze years of sunspot and flare data to create associations that can be represented using computer-based learning rules. An imaging-based real-time system that provides automated detection, grouping, and then classification of recent sunspots based on the McIntosh classification is also created and integrated within this system. The properties of the sunspot regions are extracted automatically by the imaging system and processed using the machine learning rules to generate the real-time predictions. Several performance measurement criteria are used and the results are provided in this paper. Also, quadratic score is used to compare the prediction results of ASAP with NOAA Space Weather Prediction Center (SWPC) between 1999 and 2002, and it is shown that ASAP generates more accurate predictions compared to SWPC.EPSR

Hybrid imaging and neural networks techniques for processing solar images

YesSolar imaging is currently an active area of research. A fast hybrid system for the automated detection of filaments in solar images is presented in this paper. The system includes three major stages. The central solar region is detected in the first stage using integral projections. Intensity filtering and image enhancement techniques are implemented in the second stage to enhance the quality of detection in the central region. Local detection windows are implemented in the third stage to detect the positions of filaments and to define various sized arrays to contain them. The extracted arrays are fed later to a neural network for verification purposes

Automatic Detection and Verification of Solar Features

YesA fast hybrid system for the automated detection and verification of active regions (plages) and filaments in solar images is presented in this paper. The system combines automated image processing with machine learning. The imaging part consists of five major stages. The solar disk is detected in the first stage, using a morphological hit-miss transform, watershed transform and Filling algorithm. An image-enhancement technique is introduced to remove the limb-darkening effect and intensity filtering is implemented followed by a modified region-growing technique to detect the regions of interest (RoI). The algorithms are tested on H- and CA II K3-line solar images that are obtained from Meudon Observatory, covering the period from July 2, 2001 till August 4, 2001. The detection algorithm is fast and it achieves false acceptance rate (FAR) error rate of 67% and false rejection rate (FRR) error rate of 3% for active regions, and FAR error rate of 19% and FRR error rate of 14% for filaments, when compared with the manually detected filaments in the synoptic maps. The detection performance is enhanced further using a neural network (NN), which is trained on statistical features extracted from the RoI and non-RoI. With the use of this combination the FAR has dropped to 2% for active regions and 4% for filaments.© 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 199-210, 200

Detection of Dust Storms Using MODIS Reflective and Emissive Bands

YesDust storms are one of the natural phenomena, which have increased in frequency in recent years in North Africa, Australia and northern China. Satellite remote sensing is the common method for monitoring dust storms but its use for identifying dust storms over sandy ground is still limited as the two share similar characteristics. In this study, an artificial neural network (ANN) is used to detect dust storm using 46 sets of data acquired between 2001 and 2010 over North Africa by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites. The ANN uses image data generated from Brightness Temperature Difference (BTD) between bands 23 and 31 and BTD between bands 31 and 32 with three bands 1, 3, and 4, to classify individual pixels on the basis of their multiple-band values. In comparison with the manually detection of dust storms, the ANN approach gave better result than the Thermal Infrared Integrated Dust Index approach for dust storms detection over the Sahara. The trained ANN using data from the Sahara desert gave an accuracy of 0.88 when tested on data from the Gobi desert and managed to detect 90 out of the 96 dust storm events captured worldwide by Terra and Aqua satellites in 2011 that were classified as dusty images on NASA Earth Observatory.IEEE Geoscience and Remote Sensing Societ

3D modeling of magnetic field lines using SOHO/MDI magnetogram images

YesSolar images, along with other observational data, are very important for solar physicists and space weather researchers aiming to understand the way the Sun works and affects Earth. In this study a 3D modelling technique for visualizing solar magnetic field lines using solar images is presented. Photospheric magnetic field footpoints are detected from magnetogram images and using negative and positive magnetic footpoints, dipole pairs are associated according to their proximity. Then, 3D field line models are built using the calculated dipole coordinates, and mapped to detected pairs after coordinate transformations. Final 3D models are compared to extreme ultraviolet images and existing models and the results of visual comparisons are presented

3D face recognition based on machine learning

3D facial data has a great potential for overcoming the problems of illumination and pose variation in face recognition. In this paper, we present a 3D facial system based on the machine learning. We used landmarks for feature extraction and Cascade Correlation neural network to make the final decision. Experiments are presented using 3D face images from the Face Recognition Grand Challenge database version 2.0. For CCNN using Jack-knife evaluation, an accuracy of 100% has been achieved for 7 faces with different expression, with 100% for both of specificity and sensitivity