In recent decades, photogrammetric and machine learning technologies have become essential for a better understanding of environmental and anthropic issues. The present work aims to respond one of the most topical problems in environmental photogrammetry, i.e., the automatic classification of dense point clouds using the machine learning (ML) technology for the refraction correction on the fluvial water table. The applied methodology for the acquisition of multiple photogrammetric flights was made through UAV drones, also in RTK configuration, for various locations along the Orco River, sited in Piedmont (Italy) and georeferenced with GNSS—RTK topographic method. The authors considered five topographic fluvial cross-sections to set the correction methodology. The automatic classification in ML has found a valid identification of different patterns (Water, Gravel bars, Vegetation, and Ground classes), in specific hydraulic and geomatic conditions. The obtained results about the automatic classification and refraction reduction led us the definition of a new procedure, with precise conditions of validity.

Bathymetric detection of fluvial environments through UASs and machine learning systems / Pontoglio, Emanuele; Grasso, Nives; Cagninei, Andrea; Dabove, Paolo; Camporeale, Carlo; Lingua, Andrea Maria. - In: REMOTE SENSING. - ISSN 2072-4292. - ELETTRONICO. - 12:24(2020), pp. 1-24. [10.3390/rs12244148]

Bathymetric detection of fluvial environments through UASs and machine learning systems

Pontoglio Emanuele;Grasso Nives;Cagninei Andrea;Dabove Paolo;Camporeale Carlo;Lingua Andrea Maria
2020

Abstract

In recent decades, photogrammetric and machine learning technologies have become essential for a better understanding of environmental and anthropic issues. The present work aims to respond one of the most topical problems in environmental photogrammetry, i.e., the automatic classification of dense point clouds using the machine learning (ML) technology for the refraction correction on the fluvial water table. The applied methodology for the acquisition of multiple photogrammetric flights was made through UAV drones, also in RTK configuration, for various locations along the Orco River, sited in Piedmont (Italy) and georeferenced with GNSS—RTK topographic method. The authors considered five topographic fluvial cross-sections to set the correction methodology. The automatic classification in ML has found a valid identification of different patterns (Water, Gravel bars, Vegetation, and Ground classes), in specific hydraulic and geomatic conditions. The obtained results about the automatic classification and refraction reduction led us the definition of a new procedure, with precise conditions of validity.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2859497