A pregeneration–recognition method of detecting weak seafloor echoes for full-waveform airborne LiDAR bathymetry

• Pregenerate-recognize method provides a new strategy for weak echo detection. • Determine potential seafloor echoes considering efficiency and retention. • Extract spatial features via density-related ellipsoidal neighborhoods. • Bathymetric depth, coverage, and point density are effectively improved. Full-waveform airborne LiDAR bathymetry (ALB) technology, in which the analyzed waveforms reflect the temporal positions and attribute information of targets, is effective in shallow water. However, weak seafloor echoes in full-waveform data induced by environmental and device characteristics are confused with noise signals, leading to difficulties in seafloor detection. This paper proposes a pregeneration–recognition method of detecting weak seafloor echoes for ALB. First, a two-stage local maximum algorithm is developed to identify potential seafloor echoes in waveforms and to pregenerate points. Then, an adaptive ellipsoidal neighborhood related to the point density is used to select neighborhood points, and eigenvalue-based spatial features are calculated. Finally, a back propagation neural network (BPNN) model is constructed using the points generated from surface–seafloor shots, and the seafloor points in seafloor-undefined shots are obtained by optimizing the BPNN results. The proposed method is verified on four swaths collected via the Optech Aquarius ALB system near Wuzhizhou Island and Ganquan Island in the South China Sea. The numbers of additional points detected with the proposed method near these two islands increase by 195.9 % and 40.1 % compared with the Aquarius system, which is better than the Richardson–Lucy deconvolution method. The coverages and maximum depth of seafloor points are improved and the accuracy evaluations demonstrate the credibility of the results. Therefore, the proposed pregeneration–recognition method can effectively improve the detection rate for weak seafloor echoes and the depth performance of ALB systems. Future research will focus on mitigating the impact of seafloor topography on the proposed method to expand its application scenarios.

Authors

Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2025-08-18
DOI
https://doi.org/10.1016/j.jag.2025.104798
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A pregeneration–recognition method of detecting weak seafloor echoes for full-waveform airborne LiDAR bathymetry

Yadong Guo, Xue Ji, Wenxue Xu, Fanlin Yang et al.
1 links
International Journal of Applied Earth Observation and Geoinformation
Remote Sensing and LiDAR Applications
article

A pregeneration–recognition method of detecting weak seafloor echoes for full-waveform airborne LiDAR bathymetry

Yadong Guo, Xue Ji, Wenxue Xu, Fanlin Yang, Yikai Feng, Qiuhua Tang, Yanxiong Liu
article en

Abstract

• Pregenerate-recognize method provides a new strategy for weak echo detection. • Determine potential seafloor echoes considering efficiency and retention. • Extract spatial features via density-related ellipsoidal neighborhoods. • Bathymetric depth, coverage, and point density are effectively improved. Full-waveform airborne LiDAR bathymetry (ALB) technology, in which the analyzed waveforms reflect the temporal positions and attribute information of targets, is effective in shallow water. However, weak seafloor echoes in full-waveform data induced by environmental and device characteristics are confused with noise signals, leading to difficulties in seafloor detection. This paper proposes a pregeneration–recognition method of detecting weak seafloor echoes for ALB. First, a two-stage local maximum algorithm is developed to identify potential seafloor echoes in waveforms and to pregenerate points. Then, an adaptive ellipsoidal neighborhood related to the point density is used to select neighborhood points, and eigenvalue-based spatial features are calculated. Finally, a back propagation neural network (BPNN) model is constructed using the points generated from surface–seafloor shots, and the seafloor points in seafloor-undefined shots are obtained by optimizing the BPNN results. The proposed method is verified on four swaths collected via the Optech Aquarius ALB system near Wuzhizhou Island and Ganquan Island in the South China Sea. The numbers of additional points detected with the proposed method near these two islands increase by 195.9 % and 40.1 % compared with the Aquarius system, which is better than the Richardson–Lucy deconvolution method. The coverages and maximum depth of seafloor points are improved and the accuracy evaluations demonstrate the credibility of the results. Therefore, the proposed pregeneration–recognition method can effectively improve the detection rate for weak seafloor echoes and the depth performance of ALB systems. Future research will focus on mitigating the impact of seafloor topography on the proposed method to expand its application scenarios.

International Journal of Applied Earth Observation and GeoinformationVol. 143
Life below water
Openalex Percentile: Top 81%
Remote Sensing and LiDAR Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.