A Comparison of Signal Deconvolution Algorithms Based on Small-Footprint LiDAR Waveform Simulation
A Comparison of Signal Deconvolution Algorithms Based on Small-Footprint LiDAR Waveform Simulation
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DOI：
10.1109/tgrs.2010.2103080
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发表时间：
2011-02
影响因子：
8.2
通讯作者：
Jiaying Wu;J. Aardt;G. Asner
中科院分区：
文献类型：
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作者：
Jiaying Wu;J. Aardt;G. Asner
原始入射（接收）光检测和测距（LiDAR）波形通常表现出拉伸的且相对无特征的特征，例如，LiDAR信号模糊，有效空间分辨率降低。这归因于分配用于检测的固定时间跨度、传感器的可变输出脉冲信号、接收器脉冲响应和系统噪声。理论上，这种分辨率的损失可以通过从测量的信号去卷积系统响应来恢复。在本文中，我们提出了一个比较控制的研究，即Richardson-Lucy，维纳滤波器，非负最小二乘法，为了验证哪种方法是定量上级优于其他方法。这些去卷积方法在两个用例方面进行了比较：1）基于虚拟3-D树模型的波形模拟恢复照明对象的真实横截面轮廓的能力，以及2）基于虚拟草块的波形模拟区分草本生物量的能力。本研究的所有模拟波形数据均通过“数字成像与遥感图像生成”辐射传输模拟环境获得。结果表明，Richardson-Lucy算法在恢复真实横截面的均方根误差小，检测拉伸原始波形中不可观察的局部峰的错误发现率低，以及区分草本生物量水平的分类精度高方面具有上级性能。
A raw incoming (received) Light Detection And Ranging (LiDAR) waveform typically exhibits a stretched and relatively featureless character, e.g., the LiDAR signal is smeared and the effective spatial resolution decreases. This is attributed to a fixed time span allocated for detection, the sensor's variable outgoing pulse signal, receiver impulse response, and system noise. Theoretically, such a loss of resolution can be recovered by deconvolving the system response from the measured signal. In this paper, we present a comparative controlled study of three deconvolution techniques, namely, Richardson-Lucy, Wiener filter, and nonnegative least squares, in order to verify which method is quantitatively superior to others. These deconvolution methods were compared in terms of two use cases: 1) ability to recover the true cross-sectional profile of an illuminated object based on the waveform simulation of a virtual 3-D tree model and 2) ability to differentiate herbaceous biomass based on the waveform simulation of virtual grass patches. All the simulated waveform data for this study were derived via the “Digital Imaging and Remote Sensing Image Generation” radiative transfer modeling environment. Results show the superior performance for the Richardson-Lucy algorithm in terms of small root mean square error for recovering the true cross section, low false discovery rate for detecting the unobservable local peaks in the stretched raw waveforms, and high classification accuracy for differentiating herbaceous biomass levels.