Monitoring Sea wave conditions is critical for maritime safety, coastal management, and understanding ocean-atmosphere interactions. In this study, we introduce a stereophotogrammetric system for the estimation of significant wave height (SWH), leveraging a portable and flexible optical approach for reconstructing three-dimensional sea surface topography. The proposed method employs a dual-camera system with synchronized triggering, calibrated via MATLAB stereo toolbox, and supported by a robust image processing pipeline capable of generating disparity maps and dense point clouds. This system offers an innovative, non-invasive, and scalable alternative to conventional wave monitoring tools such as buoys and radar. We present preliminary results from controlled experiments and discuss ongoing developments, including further deep-learning integration for stereo matching and potential fusion with GNSS data for georeferencing. This photogrammetric strategy aligns with the goals of the UN Agenda 2030 for Sustainable Development by promoting accessible, eco-friendly technologies for environmental observation.

Toward a Sustainable Estimation of Significant Wave Height Through Photogrammetry

Mammadova, Sabina;Ferrando, Ilaria;Sguerso, Domenico
2025-01-01

Abstract

Monitoring Sea wave conditions is critical for maritime safety, coastal management, and understanding ocean-atmosphere interactions. In this study, we introduce a stereophotogrammetric system for the estimation of significant wave height (SWH), leveraging a portable and flexible optical approach for reconstructing three-dimensional sea surface topography. The proposed method employs a dual-camera system with synchronized triggering, calibrated via MATLAB stereo toolbox, and supported by a robust image processing pipeline capable of generating disparity maps and dense point clouds. This system offers an innovative, non-invasive, and scalable alternative to conventional wave monitoring tools such as buoys and radar. We present preliminary results from controlled experiments and discuss ongoing developments, including further deep-learning integration for stereo matching and potential fusion with GNSS data for georeferencing. This photogrammetric strategy aligns with the goals of the UN Agenda 2030 for Sustainable Development by promoting accessible, eco-friendly technologies for environmental observation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1277956
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