USAGE OF CONVOLUTIONAL NEURAL NETWORK FOR MULTISPECTRAL IMAGE PROCESSING APPLIED TO THE PROBLEM OF DETECTING FIRE HAZARDOUS FOREST AREAS

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DOI: https://doi.org/10.20998/2522-9052.2019.1.19
Keywords:
deep learning, convolutional neural networks, multispectral images, spectral indices, fire hazardous forest areas

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Vladyslav Yaloveha
National Technical University "Kharkiv Polytechnic Institute", Kharkiv
https://orcid.org/0000-0001-7109-9405
Daria Hlavcheva
National Technical University "Kharkiv Polytechnic Institute", Kharkiv
https://orcid.org/0000-0001-6990-6845
Andrii Podorozhniak
National Technical University "Kharkiv Polytechnic Institute", Kharkiv
https://orcid.org/0000-0002-6688-8407

Abstract

Neural networks are intensively developed and used in all spheres of human activity in the modern world. Their use to determine the fire hazardous forest areas can begin to solve the problem of preventing wildfires. In recent years, wildfires have acquired enormous proportions. Wildfires are difficult to control and, if they occur, require a large amount of resources to eliminate them. The paper is devoted to solve the problem of identifying fire hazardous forest areas. The Camp Fire (California, USA) areas are considered. The purpose of the paper is to research the possibility of using convolutional neural networks for the detection fire hazardous forest areas using multispectral images obtained from Landsat 8. The tasks of research are finding the territories where the largest fires occurred in recent time; analyzing economic and ecologic losses from wildfires; receiving and processing multispectral images of wildfire areas from satellite Landsat 8; calculation of spectral indices (NDVI, NDWI, PSRI); developing convolutional neural network and analyzing results. The object of the research is the process of detecting fire hazardous forest areas using convolutional neural network. The subject of the research is the process of recognition multispectral images using deep learning neural network. The scientific novelty of the research is the recognition method of multispectral images by using convolutional neural network has been improved. The theory of deep learning neural networks, the theory of recognition multispectral images and mathematical statistics methods are used. The spectral indices for allocating the object under research (green vegetation, humidity, dry carbon) were calculated. It is obtained that the classification accuracy for a convolutional neural network on the test data is 94.27%.

Article Details

How to Cite
Yaloveha, V., Hlavcheva, D., & Podorozhniak, A. (2019). USAGE OF CONVOLUTIONAL NEURAL NETWORK FOR MULTISPECTRAL IMAGE PROCESSING APPLIED TO THE PROBLEM OF DETECTING FIRE HAZARDOUS FOREST AREAS. Advanced Information Systems, 3(1), 116–120. https://doi.org/10.20998/2522-9052.2019.1.19
Issue
Vol. 3 No. 1 (2019): Advanced Information Systems
Section
Intelligent information systems
Author Biographies

Vladyslav Yaloveha, National Technical University "Kharkiv Polytechnic Institute", Kharkiv

Master Student of Computer Science and Programming Department

Daria Hlavcheva, National Technical University "Kharkiv Polytechnic Institute", Kharkiv

Student of Computer Science and Programming Department

Andrii Podorozhniak, National Technical University "Kharkiv Polytechnic Institute", Kharkiv

Candidate of Technical Sciences, Associate Professor, Associate Professor of the Department of Computer Science and Programming

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