Identify Earthquake Prone Regions by Segmenting Satellite Imagery Using Vade with Multilevel Thresholding

Abstract

Introduction: Remote satellite imagery is a complex image that needs to be closely studied in detail to give appropriate alerts and to take actions. An earthquake is a serious geological hazard that can cause risk to human life and property. Due to the vast volume of satellite data, manual analysis is difficult, leading to growing interest in using intelligent computational methods to segment satellite images and identify features linked to earthquake-prone regions.

Objectives: By segmenting the satellite imagery, we can segregate the various regions based on its intensity. An optimisation problem like image segmentation is solved using evolutionary algorithms.

Methods: Differential Evolution (DE) is one such algorithm that is broadly used in optimisation, and various alternatives of this approach are developed to enhance its performance. In this work, a hybrid of the Differential Evolution algorithm named VaDE (variant Differential Evolution) is introduced and is combined with multilevel thresholding to segment satellite imagery of earthquakes based on their intensity.

Results The output obtained showed superior results compared to traditional methods.

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