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

Abstract

Neural networks are intensively developed and used in all spheres of human activity in the modern world. Their use to deter-mine 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 consid-ered. 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 statis-tics 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%.