A novel method for coastal zone bathymetry based on multisensor data fusion and unmanned systems

Area of Science:

• Geomatics Engineering
• Hydrographic Surveying
• Coastal Zone Management

Background:

• Current geospatial data fusion methods in hydrography often neglect individual measurement accuracy.
• This can lead to reduced accuracy in coastal topography maps when low-accuracy data is included.

Purpose of the Study:

• To develop a novel method for coastal bathymetric monitoring.
• To integrate multimodal geospatial data from unmanned platforms with improved accuracy.

Main Methods:

• Utilized data from Single-Beam Echo Sounder (SBES), MultiBeam EchoSounder (MBES), photogrammetry, and Light Detection and Ranging (LiDAR) (Airborne Laser Scanning - ALS, Mobile Laser Scanning - MLS).
• Implemented three processing modules: depth data processing, shallow-water data processing, and coastline determination.
• Applied a novel weighted average data fusion method, assigning weights based on measurement accuracy.

Main Results:

• The proposed method effectively minimizes redundant geospatial inputs.
• Demonstrated improved accuracy in coastal bathymetric mapping through accurate data fusion.
• The parametric model's accuracy is contingent on optimal parameter and fusion setting selection.

Conclusions:

• The novel weighted average fusion method enhances coastal bathymetric monitoring accuracy.
• Integrating diverse sensor data with accuracy-based weighting is crucial for reliable coastal topography.
• This approach offers a more robust solution for coastal zone management and mapping.