A Comparative Evaluation of Automated Solar Filament Detection

We present a comparative evaluation for automated filament detection in Hα solar images. By using metadata produced by the Advanced Automated Filament Detection and Characterization Code (AAFDCC) module, we adapted our trainable feature recognition (TFR) module to accurately detect regions in solar images containing filaments. We first analyze the AAFDCC module’s metadata and then transform it into labeled datasets for machine-learning classification. Visualizations of data transformations and classification results are presented and accompanied by statistical findings. Our results confirm the reliable event reporting of the AAFDCC module and establishes our TFR module’s ability to effectively detect solar filaments in Hα solar images.

[1]  Rafal A. Angryk,et al.  Introducing the first publicly available Content-Based Image-Retrieval system for the Solar Dynamics Observatory mission , 2013 .

[2]  Haimin Wang,et al.  Automatic Solar Filament Detection Using Image Processing Techniques , 2005 .

[3]  V. Domingo,et al.  The SOHO mission: An overview , 1995 .

[4]  Shashi Shekhar,et al.  Spatial Databases: A Tour , 2003 .

[5]  J. Banda,et al.  Framework for creating large-scale content-based image retrieval system (cbir) for solar data analysis , 2011 .

[6]  Rafal A. Angryk,et al.  An Experimental Evaluation of Popular Image Parameters for Monochromatic Solar Image Categorization , 2010, FLAIRS.

[7]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[8]  Jay Bookbinder,et al.  The transition region and coronal explorer (TRACE) , 1994 .

[9]  Pietro N. Bernasconi,et al.  Advanced Automated Solar Filament Detection And Characterization Code: Description, Performance, And Results , 2005 .

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  Krishnan Balasubramaniam,et al.  Chirality of Chromospheric Filaments , 2003 .

[12]  William D. Pence.  CFITSIO, v2.0: A New Full-Featured Data Interface , 1999 .

[13]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[14]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Artur J. Kowalski,et al.  Automatic Extraction of Filaments in Hα Solar Images , 2003 .

[16]  Petrus C. Martens,et al.  Origin and Evolution of Filament-Prominence Systems , 2001 .

[17]  Rafal A. Angryk,et al.  When Too Similar Is Bad: A Practical Example of the Solar Dynamics Observatory Content-Based Image-Retrieval System , 2013, ADBIS.

[18]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[19]  W. Pesnell,et al.  The Solar Dynamics Observatory (SDO) , 2012 .

[20]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[21]  Jay A. Bookbinder,et al.  The transition region and coronal explorer , 1998 .

[22]  N. Raouafi,et al.  Computer Vision for the Solar Dynamics Observatory (SDO) , 2012 .

[23]  Rafal A. Angryk,et al.  Selection of Image Parameters as the First Step towards Creating a CBIR System for the Solar Dynamics Observatory , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[24]  N. Japkowicz.  Learning from Imbalanced Data Sets: A Comparison of Various Strategies * , 2000 .

[25]  Karthik Ganesan Pillai,et al.  A large-scale solar image dataset with labeled event regions , 2013, 2013 IEEE International Conference on Image Processing.

[26]  J. Aboudarham,et al.  Filament Recognition and Image Cleaning on Meudon Hα Spectroheliograms , 2005 .

[27]  Haimin Wang,et al.  Development of an Automatic Filament Disappearance Detection System , 2002 .