文章基本信息

标题：ON THE USE OF ARTIFICIAL NEURAL NETWORKS IN REMOTELY PILOTED AIRCRAFT ACQUIRED IMAGES FOR ESTIMATING RESERVOIR’S BATHYMETRY
本地全文：下载
作者：Laura Coelho de Andrade ; Italo Oliveira Ferreira ; Arthur Amaral e Silva 等
期刊名称：Boletim de Ciências Geodésicas
印刷版ISSN：1982-2170
出版年度：2022
卷号：28
期号：1
语种：English
出版社：Universidade Federal do Paraná-UFPR
摘要：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.
关键词：GNSS;GPS;surface displacement;trajectory model;velocity field;GAMIT;GLOBK