A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry
A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry
复制标题
机载激光雷达测深的深度自适应波形分解方法
作者：
邢帅;王丹菂;徐青;林雨准;李鹏程;焦麟;张鑫磊;刘宸博
机载激光雷达测深（ALB）在浅水和海岸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出信号。本文提出了一种深度自适应的波形分解方法，可以用不同的模型来拟合不同深度的波形。在所提出的方法中，波形分为两类的基础上的水深，标记为“浅水（SW）”和“深水（DW）”。基于校准波形构造了一个经验波形模型（EW）用于SW波形分解，该模型比经典模型更适合SW波形分解;基于指数函数和二阶多项式模型（EFSP）构造了一个DW波形分解模型，该模型比四边形模型更适合DW波形分解。在求解模型参数时，引入了信赖域算法以提高收敛概率。该方法在两个现场数据集和两个模拟数据集上进行了测试，以评估在浅水中检测到的水面和在深水中检测到的水底的准确性。实验结果表明，与传统方法相比，该方法具有较高的信号检测率（浅水为99.11%，深水为74.64%）、较低的均方根误差（水面为0.09 m，水底为0.11 m）和较宽的水深范围（0.22 ~ 40.49 m）。
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).
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影响因子：
8.2
作者：
Jiaying Wu;J. Aardt;G. Asner
通讯作者：
Jiaying Wu;J. Aardt;G. Asner
信任域算法的最新进展
影响因子：
2.7
作者：
Ya-xiang Yuan
通讯作者：
Ya-xiang Yuan
DOI：
--
发表时间：
2007
期刊：
--
影响因子：
--
作者：
W. Wagner;A. Roncat;T. Melzer;A. Ullrich
通讯作者：
W. Wagner;A. Roncat;T. Melzer;A. Ullrich
影响因子：
2.2
作者：
Xiao Wang;Ya-xiang Yuan
通讯作者：
Xiao Wang;Ya-xiang Yuan
DOI：
10.3390/s18020552
发表时间：
2018-02-11
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Ding K;Li Q;Zhu J;Wang C;Guan M;Chen Z;Yang C;Cui Y;Liao J
通讯作者：
Liao J