Coastal Zone Mapping And Imaging Lidar Research Articles

MVCNN: A Deep Learning-Based Ocean–Land Waveform Classification Network for Single-Wavelength LiDAR Bathymetry

Ocean–land waveform classification (OLWC) is crucial in airborne LiDAR bathymetry (ALB) data processing and can be used for ocean–land discrimination and waterline extraction. However, the accuracy of OLWC for single-wavelength ALB systems is low given the nature of the green laser waveform in complex environments. Thus, in this article, a deep learning-based OLWC method called the multichannel voting convolutional neural network (MVCNN) is proposed based on the comprehensive utilization of multichannel green laser waveforms. First, multiple green laser waveforms collected in deep and shallow channels are input into a multichannel input module. Second, a one-dimensional (1-D) convolutional neural network (CNN) structure is proposed to handle each green channel waveform. Finally, a multichannel voting module is introduced to perform majority voting on the predicted categories derived by each 1-D CNN model and output the final waveform category (i.e., ocean or land waveforms). The proposed MVCNN is evaluated using the raw green laser waveforms collected by Optech coastal zone mapping and imaging LiDAR (CZMIL). Results show that the overall accuracy, kappa coefficient, and standard deviation of the overall accuracy for the OLWC utilizing green laser waveforms based on MVCNN can reach 99.41%, 0.9800, and 0.03%, respectively. Results further show that the classification accuracy of the MVCNN is improved gradually with the increase in the number of laser channels. The multichannel voting module can select the correct waveform category from the deep and shallow channels. The proposed MVCNN is highly accurate and robust, and it is slightly affected by aquaculture rafts and the merging effect of green laser waveform in very shallow waters. Thus, the use of MVCNN in OLWC for single-wavelength ALB systems is recommended. In addition, this article explores the relationships between green deep and shallow channel waveforms based on the analysis of CZMIL waveform data.

An Improved Water-Land Discriminator Using Laser Waveform Amplitudes and Point Cloud Elevations of Airborne LIDAR

Zhao, X.; Wang, X.; Zhao, J., and Zhou, F., 2021. An improved water-land discriminator using laser waveform amplitudes and point cloud elevations of airborne LIDAR. Journal of Coastal Research, 37(6), 1158–1172. Coconut Creek (Florida), ISSN 0749-0208. Laser waveforms or point clouds of airborne LIDAR are used to distinguish water and land. However, false alarms often occur in coastal areas with complex environments when laser waveforms are used. Elevations of three-dimensional (3D) point clouds can be used to help discriminate ocean and land but fail to identify inland waters. An improved water-land discriminator that uses laser waveform amplitudes and point cloud elevations is proposed in this study. First, 3D point cloud elevations derived using infrared (IR) laser are used as features to conduct ocean-land discrimination with fuzzy c-means clustering. Second, amplitudes of IR laser waveforms are used to identify inland waters from land derived by ocean-land discrimination. The proposed method is applied to data collected using Optech coastal zone mapping and imaging LIDAR (CZMIL). Land boundary derived from digital orthophoto map is used as a reference to evaluate different water-land discriminators. Results showed that consistency of the water-land interface derived using the improved water-land discriminator is higher with the reference interface than that derived by traditional waveform saturation, waveform clustering, and 3D point cloud methods. Overall accuracy of water-land points discriminated via waveform saturation, waveform clustering, 3D point cloud, and improved water-land discriminator is 93.80%, 93.84%, 95.66%, and 99.27%, respectively. High accuracy of the water-land interface and points indicates the effectiveness of the improved water-land discriminator for Optech CZMIL.

Background noise reduction for airborne bathymetric full waveforms by creating trend models using Optech CZMIL in the Yellow Sea of China.

Raw full waveforms of green lasers used in airborne LiDAR bathymetry (ALB) are contaminated by background and random noise related to the environment and ALB devices. Traditional thresholding methods have been widely used to reduce background noise in raw full waveforms on the basis of the assumption of constant background noise. However, background noise that is mainly related to background solar radiation and detector dark current changes over time. Thresholding methods perform poorly on the full waveforms with a wide variation range of background noise. A background noise reduction method considering its wide variation is proposed to decrease the background noise by creating trend models. First, each green full waveform is divided into two parts: pulse- and non-pulse-return waveforms. Second, a linear interpolation is conducted on the non-pulse-return waveform to impute the missing noise. Third, a low-pass filter is used to filter the random noise with high frequency in the imputed non-pulse-return waveform and obtain the trend model of background noise of the full waveform. Finally, the derived background noise model is used to decrease the background noise in the pulse-return waveform. The proposed method is applied to decrease the background noise in raw green full waveforms collected by the Optech coastal zone mapping and imaging LiDAR (CZMIL). The mean and standard deviation of residual noise in the CZMIL waveform reduced by the trend model of background noise are -0.03 and 3.5 digitizer counts, respectively. The proposed background noise reduction method is easy to apply and can reduce the background noise to a significantly low level. This method is recommended for preprocessing the raw full waveforms of green lasers collected by Optech CZMIL for ALB.

Modeling of Airborne Bathymetric Lidar Waveforms

ABSTRACT Kim, M.; Kopilevich, Y.; Feygels, V.; Park, J.Y., and Wozencraft, J., 2016. Modeling of airborne bathymetric lidar waveforms. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 18–30. Coconut Creek (Florida), ISSN 0749-0208. Modeling the optical power of the lidar return waveform is performed for the radiometrically calibrated CZMIL (Coastal Zone Mapping and Imaging Lidar, Optech, Inc.) data. For this purpose, a lidar waveform simulator was developed. The theory is described based on the receiver sensitivity function, the radiative transfer equation (RTE) via the Greens function, the optical reciprocity theorem, and the small angle approximation (SAA). The SAA-based RTE is solved for the radiance distribution using the Fourier transform method. Along with the numerical algorithms, the contribution was made on the air-water and water-bottom interfa...