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 10654 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.