Strategic Support Force Information Engineering University, 62 Science Road, Zhengzhou 450001, China.
Science and Technology on Near-surface Detection Laboratory, 160 Tonghui Road, Wuxi 214035, China.
Sensors (Basel). 2019 Nov 20;19(23):5065. doi: 10.3390/s19235065.
Airborne LiDAR bathymetry (ALB) has shown great potential in shallow water and coastal mapping. However, due to the variability of the waveforms, it is hard to detect the signals from the received waveforms with a single algorithm. This study proposed a depth-adaptive waveform decomposition method to fit the waveforms of different depths with different models. In the proposed method, waveforms are divided into two categories based on the water depth, labeled as "shallow water (SW)" and "deep water (DW)". An empirical waveform model (EW) based on the calibration waveform is constructed for SW waveform decomposition which is more suitable than classical models, and an exponential function with second-order polynomial model (EFSP) is proposed for DW waveform decomposition which performs better than the quadrilateral model. In solving the model's parameters, a trust region algorithm is introduced to improve the probability of convergence. The proposed method is tested on two field datasets and two simulated datasets to assess the accuracy of the water surface detected in the shallow water and water bottom detected in the deep water. The experimental results show that, compared with the traditional methods, the proposed method performs best, with a high signal detection rate (99.11% in shallow water and 74.64% in deep water), low RMSE (0.09 m for water surface and 0.11 m for water bottom) and wide bathymetric range (0.22 m to 40.49 m).
机载激光测深 (ALB) 在浅海和沿海测绘中显示出巨大的潜力。然而，由于波形的可变性，很难用单一算法从接收到的波形中检测到信号。本研究提出了一种深度自适应波形分解方法，以使用不同的模型拟合不同深度的波形。在所提出的方法中，根据水深将波形分为两类，标记为“浅水 (SW)”和“深水 (DW)”。为 SW 波形分解构建了基于校准波形的经验波形模型 (EW)，比经典模型更适合，为 DW 波形分解提出了二次多项式模型的指数函数 (EFSP)，比四边形模型表现更好。在求解模型参数时，引入信赖域算法以提高收敛概率。该方法在两个野外数据集和两个模拟数据集上进行了测试，以评估在浅水处检测到的水面和在深水处检测到的水底的准确性。实验结果表明，与传统方法相比，所提出的方法表现最佳，具有较高的信号检测率（浅水处为 99.11%，深水处为 74.64%）、较低的均方根误差（水面为 0.09 m，水底为 0.11 m）和较宽的测深范围（0.22 m 至 40.49 m）。
