The Art of Reading Explosion Phenomena: Science and Algorithms

Explosion phenomena today are considered a significant concern that needs to be detected and analyzed with a prompt response. We develop a multiclass categorization system for explosion phenomena using color images. Consequently, we describe four patterns of explosion phenomena, including pyroclastic density currents, lava fountains, lava and tephra fallout, and nuclear explosions, against three patterns of non-explosion phenomena, including wildfires, fireworks, and sky clouds. The classification task was handled through extracting different types of features, including texture features, amplitude features, frequency features, and histogram features. Then, these features were fed into several multiclass classification methods. In addition, we present a new data set for volcanic and nuclear explosions that includes 10 654 samples. Evaluation results show the one-against-one multiclass support vector machine with degree 3 polynomial kernel outperforms other classification methods. It produces the highest classification rate of 90.85% to categorize 5327 images of the data set. A reasonable execution time of approximately 117 ms was accomplished to classify one input test image.

[1]  Prabhat,et al.  Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets , 2016, ArXiv.

[2]  H. Dickinson,et al.  Systems for the detection and identification of nuclear explosions in the atmosphere and in space , 1965 .

[3]  Thomas Shea,et al.  Column collapse and generation of pyroclastic density currents during the A.D. 79 eruption of Vesuvius: The role of pyroclast density , 2011 .

[4]  Robert I. Tilling,et al.  Eruptions of Hawaiian Volcanoes: Past, Present, and Future , 1987 .

[5]  Keith Jack,et al.  Video Demystified: A Handbook for the Digital Engineer , 1993 .

[6]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Di Huang,et al.  Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  Ajay S. Iyer,et al.  Neural classification of infrasonic signals associated with hazardous volcanic eruptions , 2011, The 2011 International Joint Conference on Neural Networks.

[9]  Salvatore Stramondo,et al.  Volcanic ash detection and retrievals using MODIS data by means of neural networks , 2011 .

[10]  M. Alexander,et al.  Global estimates of gravity wave parameters from GPS radio occultation temperature data , 2010 .

[11]  Renato Campanini,et al.  Synopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data at Mt Etna, Italy , 2009 .

[12]  Christopher J. Mocella,et al.  Chemistry of Pyrotechnics , 2019 .

[13]  Matthew Blackett,et al.  An Overview of Infrared Remote Sensing of Volcanic Activity , 2017, J. Imaging.

[14]  James S. Monroe,et al.  Essentials of Physical Geology , 2008 .

[15]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  B. Ando,et al.  A Measurement Tool for Investigating the Volcanic Activity , 2005, 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings.

[17]  M. Jinguuji,et al.  Quantitative estimation of erupted volcanic ash by using satellite image , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[18]  Michael P. Poland,et al.  Instrumentation Recommendations for Volcano Monitoring at U.S. Volcanoes Under the National Volcano Early Warning System , 2008 .

[19]  O. A. Braitseva,et al.  Catastrophic eruptions of the directed-blast type at Mount St , 1985 .

[20]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[21]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Frank R. Spellman.  Geography for Nongeographers , 2010 .

[23]  S. Rapuano,et al.  An introduction to FFT and time domain windows , 2007, IEEE Instrumentation & Measurement Magazine.

[24]  Peter Kokelaar,et al.  Pyroclastic density currents and the sedimentation of ignimbrites , 2002 .

[25]  Rasoul,et al.  The volcano adventure guide , 2005 .

[26]  Khaled M. Elleithy,et al.  A Novel Vision-Based Classification System for Explosion Phenomena , 2017, J. Imaging.

[27]  Bernard W. Pipkin,et al.  Geology and the environment , 1994 .

[28]  S. Self.  The effects and consequences of very large explosive volcanic eruptions , 2006, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[29]  Higinio Mora-Mora,et al.  μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras , 2015, Expert Syst. Appl..

[30]  John V. Rock,et al.  Jan ? ? ? ? ? ? , 2002 .

[31]  Simone Schweitzer.  Nuclear Arms Race Technology And Society , 2016 .

[32]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Hiroki Nakahara,et al.  Identification of inpainted satellite images using evalutionary artificial neural network (EANN) and k-nearest neighbor(KNN) algorithm , 2017, 2017 8th International Conference of Information and Communication Technology for Embedded Systems (IC-ICTES).

[35]  Yann LeCun,et al.  Very Deep Convolutional Networks for Text Classification , 2016, EACL.

[36]  Winthrop W. Smith.  The Fast Fourier Transform , 1995 .