Classification of Video Observation Data for Volcanic Activity Monitoring Using Computer Vision and Modern Neural NetWorks (on Klyuchevskoy Volcano Example)

Sergey Korolev,Igor Urmanov,Aleksei Sorokin,Aleksandr Kamaev,Olga Girina

doi:10.3390/rs13234747

Abstract

Currently, video observation systems are actively used for volcano activity monitoring. Video cameras allow us to remotely assess the state of a dangerous natural object and to detect thermal anomalies if technical capabilities are available. However, continuous use of visible band cameras instead of special tools (for example, thermal cameras), produces large number of images, that require the application of special algorithms both for preliminary filtering out the images with area of interest hidden due to weather or illumination conditions, and for volcano activity detection. Existing algorithms use preselected regions of interest in the frame for analysis. This region could be changed occasionally to observe events in a specific area of the volcano. It is a problem to set it in advance and keep it up to date, especially for an observation network with multiple cameras. The accumulated perennial archives of images with documented eruptions allow us to use modern deep learning technologies for whole frame analysis to solve the specified task. The article presents the development of algorithms to classify volcano images produced by video observation systems. The focus is on developing the algorithms to create a labelled dataset from an unstructured archive using existing and authors proposed techniques. The developed solution was tested using the archive of the video observation system for the volcanoes of Kamchatka, in particular the observation data for the Klyuchevskoy volcano. The tests show the high efficiency of the use of convolutional neural networks in volcano image classification, and the accuracy of classification achieved 91%. The resulting dataset consisting of 15,000 images and labelled in three classes of scenes is the first dataset of this kind of Kamchatka volcanoes. It can be used to develop systems for monitoring other stratovolcanoes that occupy most of the video frame.

Highlights

There are 30 active volcanoes in Kamchatka
Considering the described peculiarities, we proposed to use methods based on the convolutional neural networks (CNN) as a tool for classifying images, which allows to analyse the whole frame, without highlighting the area of interest and preliminary feature vector extraction
The EfficientNet convolutional neural network trained on labelled dataset shows the average accuracy of 91% in classification daytime images

Summary

Introduction

There are 30 active volcanoes in Kamchatka. Effusive and extrusive eruptions take place in this region, during which tons of volcanic products in the form of lava, pyroclastics, volcanic gases and aerosols come to the surface of the earth. These natural phenomena have an impact on the environment and pose a threat to the population and air traffic in the Pacific Northwest [1]. Due to the geographic location of volcanoes and insufficient ground-based scientific infrastructure, at present, the main type of their instrumental observations is Earth remote sensing systems. Based on new technologies and data sources, the VolSatView information system was created [5]

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Conclusion