Estimation of Physical Parameters of Waveforms With Neural Networks
Saad Ahmed Jamal Thomas Corpetti Dirk Tiede Mathilde Letard Dimitri Lague
Light Detection and Ranging (LiDAR) are fast emerging sensors in the field of
Earth Observation. It is a remote sensing technology that utilizes laser beams
to measure distances and create detailed three-dimensional representations of
objects and environments. The potential of Full Waveform LiDAR is much greater
than just height estimation and 3D reconstruction only. Overall shape of signal
provides important information about properties of water body. However, the
shape of FWL is unexplored as most LiDAR software work on point cloud by
utilizing the maximum value within the waveform. Existing techniques in the
field of LiDAR data analysis include depth estimation through inverse modeling
and regression of logarithmic intensity and depth for approximating the
attenuation coefficient. However, these methods suffer from limitations in
accuracy. Depth estimation through inverse modeling provides only approximate
values and does not account for variations in surface properties, while the
regression approach for the attenuation coefficient is only able to generalize
a value through several data points which lacks precision and may lead to
significant errors in estimation. Additionally, there is currently no
established modeling method available for predicting bottom reflectance. This
research proposed a novel solution based on neural networks for parameter
estimation in LIDAR data analysis. By leveraging the power of neural networks,
the proposed solution successfully learned the inversion model, was able to do
prediction of parameters such as depth, attenuation coefficient, and bottom
reflectance. Performance of model was validated by testing it on real LiDAR
data. In future, more data availability would enable more accuracy and
reliability of such models.