Gold – A novel deconvolution algorithm with optimization for waveform LiDAR processing
Gold – A novel deconvolution algorithm with optimization for waveform LiDAR processing
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DOI：
10.1016/j.isprsjprs.2017.04.021
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发表时间：
2017-07
影响因子：
12.7
通讯作者：
Tan Zhou;S. Popescu;Keith Krause;R. Sheridan;E. Putman
中科院分区：
文献类型：
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作者：
Tan Zhou;S. Popescu;Keith Krause;R. Sheridan;E. Putman
波形光探测和测距（LiDAR）数据在精确表征植被结构方面优于离散回波LiDAR数据。然而，我们缺乏对不同地形和植被条件下的波形数据处理方法的全面了解。本文的目的是突出一种新的反卷积算法，黄金算法，处理波形LiDAR数据与最佳的反卷积参数。此外，我们提出了一个波形处理方法的比较研究，以提供洞察选择一个给定的组合的植被和地形特征的方法。我们采用了两种波形处理方法：（1）直接分解，（2）反褶积和分解。在方法二中，我们使用两种去卷积算法-Richardson-Lucy（RL）算法和Gold算法。对不同方法得到的最终产品（如数字地形模型（DTM）和冠层高度模型（CHM））的回波数量、定位精度、与相应参考数据的偏差沿着参数不确定性进行了全面定量的比较。本研究在三个研究地点进行，其中包括不同的生态区域、植被和海拔梯度。结果表明，两种反褶积算法对输入数据的预处理步骤都很敏感。反卷积和分解方法更能够以较低的假回波检测率检测隐藏回波，特别是对于Gold算法。与参考数据相比，所有方法都能获得令人满意的精度评估结果，其平均空间差异（DTMs <1.22 m，CHMs <0.77 m）和均方根误差（RMSE）（DTMs <1.26 m，CHMs <1.93 m）都很小。更具体地说，Gold算法上级其他算法，具有较小的均方根误差（RMSE）（<1.01 m），而直接分解方法在0.5和1 m范围内的空间差异百分比方面效果更好。参数不确定性分析表明，Gold算法在植被密集地区的RMSE最小，优于其他算法; RL算法在植被稀疏地区的RMSE最小，优于其他算法。此外，高水平的不确定性更多地发生在高坡度和高植被的地区。该研究为波形处理提供了一种新的方法，将有利于波形LiDAR数据的高保真处理，以表征植被结构。
Waveform Light Detection and Ranging (LiDAR) data have advantages over discrete-return LiDAR data in accurately characterizing vegetation structure. However, we lack a comprehensive understanding of waveform data processing approaches under different topography and vegetation conditions. The objective of this paper is to highlight a novel deconvolution algorithm, the Gold algorithm, for processing waveform LiDAR data with optimal deconvolution parameters. Further, we present a comparative study of waveform processing methods to provide insight into selecting an approach for a given combination of vegetation and terrain characteristics. We employed two waveform processing methods: (1) direct decomposition, (2) deconvolution and decomposition. In method two, we utilized two deconvolution algorithms – the Richardson-Lucy (RL) algorithm and the Gold algorithm. The comprehensive and quantitative comparisons were conducted in terms of the number of detected echoes, position accuracy, the bias of the end products (such as digital terrain model (DTM) and canopy height model (CHM)) from the corresponding reference data, along with parameter uncertainty for these end products obtained from different methods. This study was conducted at three study sites that include diverse ecological regions, vegetation and elevation gradients. Results demonstrate that two deconvolution algorithms are sensitive to the pre-processing steps of input data. The deconvolution and decomposition method is more capable of detecting hidden echoes with a lower false echo detection rate, especially for the Gold algorithm. Compared to the reference data, all approaches generate satisfactory accuracy assessment results with small mean spatial difference (<1.22 m for DTMs, <0.77 m for CHMs) and root mean square error (RMSE) (<1.26 m for DTMs, <1.93 m for CHMs). More specifically, the Gold algorithm is superior to others with smaller root mean square error (RMSE) (<1.01 m), while the direct decomposition approach works better in terms of the percentage of spatial difference within 0.5 and 1 m. The parameter uncertainty analysis demonstrates that the Gold algorithm outperforms other approaches in dense vegetation areas, with the smallest RMSE, and the RL algorithm performs better in sparse vegetation areas in terms of RMSE. Additionally, the high level of uncertainty occurs more on areas with high slope and high vegetation. This study provides an alternative and innovative approach for waveform processing that will benefit high fidelity processing of waveform LiDAR data to characterize vegetation structures.