| Revista: | Boletim de ciencias geodesicas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000457325 |
| ISSN: | 1413-4853 |
| Autores: | Andrade, Laura Coelho de1 Ferreira, Italo Oliveira1 Silva, Arthur Amaral e1 Gibrim, Victoria Teixeira1 Santos, Felipe Catao Mesquita1 |
| Instituciones: | 1Universidade Federal de Vicosa, Departamento de Engenharia Civil, Vicosa, Minas Gerais. Brasil |
| Año: | 2022 |
| Volumen: | 28 |
| Número: | 1 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Analítico, descriptivo |
| Resumen en inglés | The use of acoustic systems for mapping submerged areas is the most accurate way. However, echosounders are expensive and, in addition, the equipment requires a great deal of experience on the part of the specialist. From another perspective, orbital and aerial images (acquired by RPA’s- Remotely Piloted Aircraft) can offer bathymetric maps of larger locations that are difficult to access at a low operating cost. Therefore, the present study’s main objective was to evaluate the utility of RGB images obtained with RPA’s in water reservoirs. Thus, Artificial Neural Networks were used for depth training and prediction. Subsequently, it compared to the bathymetric data from the same pond in question, raised from acoustic sensors, quantifying the vertical uncertainty through three estimators. Regarding the statistical analysis, the RMSE and Ф estimators showed better reliability. The 300-point sample showed the best quality in processing. The results showed that the methodology could improve the management of water resources. The method allows reduced execution time and lowers cost, especially for using only the green, red and blue channels, easily found in most cameras coupled to RPA’s |
| Disciplinas: | Geociencias |
| Palabras clave: | Hidrología, Gestión de recursos hídricos, Mapeo sumergido, Aeronaves pilotadas remotamente, Imágenes RGB, Redes neuronales artificiales |
| Keyword: | Hydrology, Water resources management, Submerged mapping, Remotely piloted aircraft, RGB images, Artificial Neural Networks |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
