On-Site Stability Assessment of Rubble Mound Breakwaters Using Unmanned Aerial Vehicle-Based Photogrammetry and Random Sample Consensus

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Arza García, M (Autor) Outra; Jose A Goncalves (Autor) FCUP; Ferreira Pinto, V (Autor) Outra; Bastos, G (Autor) Outra

Revista

Título: Remote Sensing

Vol. 16

Página Final: 331

ISSN: 2072-4292

Editora: MDPI

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-Y0T

DOI: 10.3390/rs16020331

Abstract (EN): Traditional methods for assessing the stability of rubble mound breakwaters (RMBs) often rely on 2.5D data, which may fall short in capturing intricate changes in the armor units, such as tilting and lateral shifts. Achieving a detailed analysis of RMB geometry typically requires fully 3D methods, but these often hinge on expensive acquisition technologies like terrestrial laser scanning (TLS) or airborne light detection and ranging (LiDAR). This article introduces an innovative approach to evaluate the structural stability of RMBs by integrating UAV-based photogrammetry and the random sample consensus (RANSAC) algorithm. The RANSAC algorithm proves to be an efficient and scalable tool for extracting primitives from point clouds (PCs), effectively addressing challenges presented by outliers and data noise in photogrammetric PCs. Photogrammetric PCs of the RMB, generated using Structure-from-Motion and MultiView Stereo (SfM-MVS) from both pre- and post-storm flights, were subjected to the RANSAC algorithm for plane extraction and segmentation. Subsequently, a spatial proximity criterion was employed to match cuboids between the two time periods. The methodology was validated on the detached breakwater of Cabedelo do Douro in Porto, Portugal, with a specific focus on potential rotations or tilting of Antifer cubes within the protective layer. The results, assessing the effects of the Leslie storm in 2018, demonstrate the potential of our approach in identifying and quantifying structural changes in RMBs. © 2024 by the authors.

Idioma: Inglês

Tipo (Avaliação Docente): Científica