This paper is concerned with the decomposition of bathymetric lidar waveforms. Because of the presence of water, processing such data remains a challenge since water impacts their shape and signal-to-noise ratio, depending in particular on the associated turbidity. In this paper, we explore the use of attentive autoencoders to decompose bathymetric waveforms and recover their air/water interface, water column, and water bottom components simultaneously, without relying on assumptions about the impulse or target surface nature. On simulated waveforms, the method achieves lower decomposition error than existing approaches, handling overlapping echoes of very shallow waters and weak returns in deeper water. This opens to attractive strategies to process real bathymetric waveforms.
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Bathymetric LiDAR Waveform Decomposition with Temporal Attentive Encoder-Decoders
Semantic Scholar · Environmental Science · 2023
Abstract
This paper is concerned with the decomposition of bathymetric lidar waveforms. Because of the presence of water, processing such data remains a challenge since water impacts their shape and signal-to-noise ratio, depending in particular on the associated turbidity. In this paper, we explore the use of attentive autoencoders to decompose bathymetric waveforms and recover their air/water interface, water column, and water bottom components simultaneously, without relying on assumptions about the impulse or target surface nature. On simulated waveforms, the method achieves lower decomposition error than existing approaches, handling overlapping echoes of very shallow waters and weak returns in deeper water. This opens to attractive strategies to process real bathymetric waveforms.