Fusion of Satellite and Drone Images for Identifying Water Bodies

Ariane Paula Barros; Elizângela Jussara dos Santos; Fernanda Kristen da Silva Pedro; Dimas Augusto Mendes Lemes; José Picolo; Guilherme Ribeiro Sales; Valentino Corso; Cides Semprebom Bezerra

doi:10.22456/2175-2745.143523

Authors

Ariane Paula Barros CPQD - Centro de Pesquisa e Desenvolvimento https://orcid.org/0009-0009-7420-4798
Elizângela Jussara dos Santos CPQD - Centro de Pesquisa e Desenvolvimento
Fernanda Kristen da Silva Pedro CPQD - Centro de Pesquisa e Desenvolvimento https://orcid.org/0009-0003-7336-7786
Dimas Augusto Mendes Lemes Pontifícia Universidade Católica de Campinas (PUCC) https://orcid.org/0000-0001-5189-4129
José Picolo Pontifícia Universidade Católica de Campinas (PUCC) https://orcid.org/0009-0002-0214-4932
Guilherme Ribeiro Sales CPQD - Centro de Pesquisa e Desenvolvimento https://orcid.org/0009-0004-7994-7973
Valentino Corso CPQD - Centro de Pesquisa e Desenvolvimento https://orcid.org/0009-0004-5253-5151
Cides Semprebom Bezerra CPQD - Centro de Pesquisa e Desenvolvimento

DOI:

https://doi.org/10.22456/2175-2745.143523

Keywords:

Image Fusion, Remote Sensing, Drone, Riverine Systems, High Resolution

Abstract

Mapping areas with water bodies is crucial for monitoring water systems and resource management, as well as for guiding urban expansion and land-use planning, thus contributing to sustainable resource management, flood prevention, and the conservation of aquatic ecosystems. While satellite images offer broad territorial coverage, drones provide high spatial resolution of the Earth's surface. In this research, the Pulse Coupled Neural Network (PCNN) [1] image fusion method and the GDAL Pansharpening method, the latter being available in QGIS, were compared to determine which is more effective in merging images to highlight water surface areas in a case study, combining the spectral coverage of satellites with the precision of drones. The implemented PCNN model was able to overcome QGIS Pansharpening method, which is based on Brovey algorithm, obtaining better metrics. This study of drone and satellite image fusion has shown to be a promising approach to overcome the limitations of these technologies when used individually.

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