Investigation of the effectiveness of the architecture of convolutional neural networks on satellite images

Vladislav V. Dovgal; Dmitry A. Gura; Roman A. Dyachenko

doi:10.1117/12.3025174

20 February 2024 Investigation of the effectiveness of the architecture of convolutional neural networks on satellite images

Vladislav V. Dovgal, Dmitry A. Gura, Roman A. Dyachenko

Author Affiliations +

Proceedings Volume 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023); 1306512 (2024) https://doi.org/10.1117/12.3025174
Event: Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 2023, Dushanbe, Tajikistan

Abstract

To date, the problem of automating work with images taken using satellite systems has become relevant. This task concerns a wide range of human activities, including urban planning, transport logistics, ecology and environmental monitoring, etc. To solve these problems, there are many tools, of which solutions based on the use of machine learning algorithms are particularly effective. The complexity of this approach lies in the wide variety of computer vision models that exist today. The purpose of this research работы is to select the most popular neural network architectures and conduct a study that aims to identify the most effective architecture in terms of efficiency and quality of work performed. This study will help determine the machine learning model that is most suitable for further use in a software product aimed at working with satellite images, the main functions of which will be object detection and segmentation.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Vladislav V. Dovgal, Dmitry A. Gura, and Roman A. Dyachenko "Investigation of the effectiveness of the architecture of convolutional neural networks on satellite images", Proc. SPIE 13065, Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), 1306512 (20 February 2024); https://doi.org/10.1117/12.3025174

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KEYWORDS

Image segmentation

Earth observing sensors

Satellite imaging

Satellites

Education and training

Convolutional neural networks

Object detection

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