radioelectronicsИзвестия высших учебных заведений России. РадиоэлектроникаJournal of the Russian Universities. Radioelectronics1993-89852658-4794Saint Petersburg Electrotechnical University10.32603/1993-8985-2026-29-3-112-120radioelectronics-1170Research ArticleПРИБОРЫ МЕДИЦИНСКОГО НАЗНАЧЕНИЯ, КОНТРОЛЯ СРЕДЫ, ВЕЩЕСТВ, МАТЕРИАЛОВ И ИЗДЕЛИЙMEDICAL DEVICES, ENVIRONMENT, SUBSTANCES, MATERIAL AND PRODUCTIntegrating Radar and Multispectral Data from Sentinel Satellites for Accurate Urban Vegetation Classification using Deep Learning MethodsIntegrating Radar and Multispectral Data from Sentinel Satellites for Accurate Urban Vegetation Classification using Deep Learning Methodshttps://orcid.org/0009-0004-6564-0743Ngoua Ndong AveleJ. B.Ngoua Ndong AveleJ. B.

Jacques B. Ngoua Ndong Avele, Bachelor in Omar Bongo University, Libreville, Gabon, Master's degree in Quantum and Optical Electronics (2024, Saint Petersburg Electrotechnical University). Postgraduate student of the Department of Radio Engineering Systems in Radio Navigation and Radar

5 F, Professor Popov St., St Petersburg 197022 

Jacques B. Ngoua Ndong Avele, Bachelor in Omar Bongo University, Libreville, Gabon, Master's degree in Quantum and Optical Electronics (2024, Saint Petersburg Electrotechnical University). Postgraduate student of the Department of Radio Engineering Systems in Radio Navigation and Radar

5 F, Professor Popov St., St Petersburg 197022 

avelejacques@yahoo.frOmar Bongo University ; Saint Petersburg Electrotechnical UniversityГабонOmar Bongo University ; Saint Petersburg Electrotechnical UniversityGabon202614072026293112120Copyright © Ngoua Ndong Avele J., 20262026Ngoua Ndong Avele J.Ngoua Ndong Avele J.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://re.eltech.ru/jour/article/view/1170

Introduction. The Sentinel-1 (equipped with synthetic aperture radar) and Sentinel-2 (equipped with multispectral cameras) satellites are valuable tools for environmental monitoring, particularly for assessing vegetation cover and soil erosion. The data obtained by these systems can be processed using machine learning techniques to generate accurate vegetation classification maps.Aim. To develop and evaluate machine learning models capable of efficiently fusing Sentinel satellite data to produce more accurate and detailed maps of urban vegetation, essential for urban planning, environmental monitoring, and climate change mitigation. The integration of Sentinel-1 and Sentinel-2 satellite data is intended to overcome the limitations associated with using each data type in isolation, particularly in complex urban environments where spectral signatures can be ambiguous and radar provides only structural information.Materials and methods. A classification map of urban vegetation on Kotlin Island (St Petersburg) was generated by integrating data from Sentinel-1 (synthetic aperture radar) and Sentinel-2 (multispectral imagery) using the Random Forest algorithm, Tensorflow and Sklearn Python libraries. Conventional urban vegetation mapping often relies on a single data source, leading to limited accuracy and inability to differentiate subtle vegetation types. The vegetation classes considered in this research were coniferous forest, deciduous forest, wetland vegetation, and coastal meadows.Results. The integrated analysis of radar and multispectral data enabled more accurate identification of erosion-prone zones in non-vegetated areas and more reliable estimation of plant moisture content. Such a fusion approach showed significantly improved classification accuracy and reduced error rate compared to techniques relying on individual indices.Conclusion. The fusion method demonstrated superior performance in classifying vegetation types, which confirms its potential for applications in remote sensing and environmental monitoring. Future research will focus on integrating radar and multispectral data from Sentinel satellites for urban vegetation classification using the Support Vector Machine algorithm.

Introduction. The Sentinel-1 (equipped with synthetic aperture radar) and Sentinel-2 (equipped with multispectral cameras) satellites are valuable tools for environmental monitoring, particularly for assessing vegetation cover and soil erosion. The data obtained by these systems can be processed using machine learning techniques to generate accurate vegetation classification maps.Aim. To develop and evaluate machine learning models capable of efficiently fusing Sentinel satellite data to produce more accurate and detailed maps of urban vegetation, essential for urban planning, environmental monitoring, and climate change mitigation. The integration of Sentinel-1 and Sentinel-2 satellite data is intended to overcome the limitations associated with using each data type in isolation, particularly in complex urban environments where spectral signatures can be ambiguous and radar provides only structural information.Materials and methods. A classification map of urban vegetation on Kotlin Island (St Petersburg) was generated by integrating data from Sentinel-1 (synthetic aperture radar) and Sentinel-2 (multispectral imagery) using the Random Forest algorithm, Tensorflow and Sklearn Python libraries. Conventional urban vegetation mapping often relies on a single data source, leading to limited accuracy and inability to differentiate subtle vegetation types. The vegetation classes considered in this research were coniferous forest, deciduous forest, wetland vegetation, and coastal meadows.Results. The integrated analysis of radar and multispectral data enabled more accurate identification of erosion-prone zones in non-vegetated areas and more reliable estimation of plant moisture content. Such a fusion approach showed significantly improved classification accuracy and reduced error rate compared to techniques relying on individual indices.Conclusion. The fusion method demonstrated superior performance in classifying vegetation types, which confirms its potential for applications in remote sensing and environmental monitoring. Future research will focus on integrating radar and multispectral data from Sentinel satellites for urban vegetation classification using the Support Vector Machine algorithm.

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The authors declare that there are no conflicts of interest present.