Implementation of satelite image classification using PyTorch

Abstract

This article presents a practical implementation of the satellite image classification task using the PyTorch deep learning library. The aim of the work is to develop a neural network model capable of classifying satellite images into four classes: water, sands, clouds, and greenery. The model is trained using the pre-trained ResNet architecture. The proposed approach allows for automatic classification of satellite images, fa-cilitating the analysis of large amounts of data, and has been tested on Sentinel-2 satellite images acquired from March to August 2023 for parts of Kherson and Mykolaiv regions, Ukraine, and achieved an average accuracy of 95%. The presented research demonstrates the practical use of modern deep learning methods, in particular the PyTorch library, to solve actual problems of satellite image analysis and classification.