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在遥感探测过程中,由于目标的光谱反射率不同,超连续谱激光源能量分布也不均匀,回波信号在不同光谱通道之间通常会有明显的变化[3,5,12]。这对波形参数的提取,即目标的几何位置和光谱参数获取有很大影响,特别是对微弱信号或重叠信号。此外,各光谱通道的脉冲回波到达时间不是严格同步的,影响几何信息的精确检索[3,5]。因此,考虑到各光谱通道回波波形的空间相关性和形状特征,提出了一种高光谱全波形激光雷达回波波形数据处理方法,以获取先验知识为前提,进一步提升多目标波形分量的提取精度。该方法分为回波预处理,先验知识获取,多通道波形分解与优化三个步骤,如图1所示。
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回波预处理部分包括三部分的工作,分别是噪声的评估和阈值计算、滤波降噪和时域同步校正。噪声的评估和阈值计算用于后续波形分解时作为分解的停止线;滤波降噪是减少传输过程中的多种噪声对波形分量的影响;时域同步校正是因为多个通道的发射脉冲和回波的时间不同,需要进行校正以后在多通道中才存在可比较性。
噪声评估和阈值计算中,首先在原始回波数据中确定有效回波波形所处位置和范围,在有效波形范围外的数据中采用均值滤波的方式通过对数据点进行滑动窗平均计算来估计每个点周围的噪声级别,通过所有位置噪声级别的估计结果平均以确定该组数据噪声阈值。
在下一步的滤波降噪中,文中采用Savitzky-Golay滤波算法[15],通过拟合多项式函数来对原始数据进行平滑处理,从而消除噪声和抖动,保留数据信号的主要特征。为了确保保留尽可能详细的信号细节特征,文中选取窗口大小为9,多项式阶数为3。
为了保证各通道回波数据之间的时间匹配,在滤波处理后,采用多个通道的发射脉冲与回波数据进行时间校正,由于高光谱激光雷达系统(Hyperspectral LiDAR,HSL)各通道所记录的波形数据在时间上存在差异[3,5,16],为防止出现不同通道下同一目标对应的距离差距过大而造成错检,需要在时域上对所有通道的波形数据进行校正。在去除拟合程度低、记录误差大的通道的回波数据之后,以剩余所有通道发射脉冲中心时间的均值$ {\overline s_{trans}} $统一作为发射脉冲时间的起点,计算各个通道发射脉冲的中心时间$ {s}_{{\lambda }_{j},trans} $与$ {\overline{s}}_{trans} $的差值,将整个通道的距离按照差值平移到相应的位置。
$$ \Delta {t_{{\lambda _j}}} = {s_{{\lambda _j},trans}} - {\overline s_{trans}} $$ (1) 式中:$ \Delta {t}_{{\lambda }_{j}} $代表第$ j $个通道(对应波长$ \lambda $)的波形数据需要整体平移的时间;$ \Delta {t_{{\lambda _j}}} < 0 $表示发射脉冲中心时间在起点的左边,需要向右平移,$ \Delta {t}_{{\lambda }_{j}} > 0 $则反之需要向左平移。
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先验知识的获取主要是对激光足印中包含的目标数量的分析。影响回波波形的因素有目标的个数、表面特性、光谱等多种因素。例如当两个光谱反射率不同且间距很近的目标时,由于两种目标在同一波长下反射率不同,因此不同波长下两目标回波叠加以后其中心位置将会发生变化,虽然回波看起来与单目标回波波形相似,但实际上是由于目标间距小于脉冲宽度所对应的距离分辨单元。
因此文中采用多光谱通道回波波形进行统计分析,利用偏正态函数首先将各个通道的回波波形进行拟合,对于包含多目标的回波,通过分析回波中心位置、脉宽和偏度等多种参数,从光谱维发现这些参数与仅包含一个目标的回波的拟合参数的不同之处,以达到获取光斑中获取目标数量的目的。同时,由于已经经过最初偏正态模型的拟合,获取的参数可为下一步的波形分解提供初始参数的参考。
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根据上述步骤获取到波形分解的初始参数,参考后能够按照明确的单目标分解和多目标分解分别展开多通道的波形分解与优化。当先验知识表示当前回波数据为单目标数据时,通过对比在不同波长通道波形的中心位置参数,根据其信噪比进行加权平均得到目标更加精确的距离信息;当先验知识表示当前回波为多目标,则首先对根据目标数量对多个目标所对应子回波的初始参数进行估计,基于信赖域参数优化[17]进行各个波长通道的波形分解计算并对分解结果的平均误差等评价参数进行计算分析,通过对分解结果设置阈值发现分解错误的通道调整初始参数进行重新分解,通过通道间波形的相互校正获取真正的波形分量参数集,达到多通道波形分解和优化的目的。
Lidar point cloud expansion and identification method for masking targets based on time-spectra information
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摘要: 在利用三维激光雷达点云进行目标描述时,人们通常通过点云插值等方式描述目标细节,通过点云分类标识区分目标种类。高光谱全波形激光雷达能够通过波形分解和光谱重构实现上述功能,然而当激光束内存在多个目标形成遮蔽关系时,由于间距较近以及光斑分裂等原因,难以准确获取目标时间-光谱信息,从而无法较为精准地反演目标几何位置和反射率分布信息。文中提出了一种高光谱回波波形分解方法以及相应的点云扩展和标识方法,实现了优于激光脉宽分辨距离的波形分解和更准确的光谱重构。实验结果表明:在密集遮蔽条件下,该方法仍能达到约3倍的点云扩展效果和准确的目标分类标识。这种精准的点云扩展和标识方法能够为基于点云数据的探测、遥感情报生成提供良好的数据支撑。
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关键词:
- 激光遥感 /
- 激光三维点云 /
- 高光谱全波形激光雷达 /
- 波形分解
Abstract:Objective With rich data and wide scanning range, three-dimensional lidar is widely used in obstacle detection, terrain reconstruction, target detection, classification and tracking, as well as forestry and agricultural remote sensing. When using point cloud data for object description, people usually describe the details of objects through point cloud interpolation, and distinguish the types of objects through point cloud classification identifiers. The above functions can be achieved with high spectral full waveform lidar through waveform decomposition and spectral reconstruction. However, when there are multiple targets in the laser beam forming a shielding relationship, due to the close spacing and light spot splitting, it is difficult to accurately obtain target time-spectral information to reverse the target position and reflectivity distribution. For this purpose, we design a hyperspectral waveform decomposition method and corresponding point cloud expansion and identification method. Methods Considering the spatial correlation and shape characteristics of the echo waveforms of various spectral channels, a new hyperspectral full waveform lidar waveform data processing method is proposed (Fig.1). Based on the prior knowledge obtained from multi-channel echo waveform comparison, inter channel correction is used to further improve the extraction accuracy of multi-target waveform components. Based on this new waveform decomposition method, the time-spectral information of the target can be accurately obtained. Accordingly, a point cloud extension and point cloud identification methods based on principal component analysis and random forest algorithm are proposed (Fig.2). Results and Discussions With a full-waveform hyperspectral lidar, camouflage nets, and two diffuse reflective plates with known reflectivity, point cloud expansion and identification verification experiments were conducted. The experiment broke through the range resolution limited by pulse width under dense shielding conditions, resulting in a triple expansion of point cloud data on the target board. The target reflectance spectrum recovered by the proposed algorithm has a high similarity to the actual reflectance spectrum of the target, and the point cloud identification result perfectly distinguishes two target plates at the same distance. Conclusions A new hyperspectral waveform decomposition scheme is proposed to solve the problems encountered in the generation of three-dimensional point clouds for hyperspectral full waveform lidar during the detection of obscured targets, such as the difficulty in resolving close range targets, and the inaccurate acquisition of spectral information caused by light spot splitting. Based on this, a method for expanding and identifying point cloud data is proposed. Experimental results show that even under dense camouflage nets, accurate waveform decomposition and spectral reconstruction can be achieved with the proposed waveform processing method, thereby achieving point cloud expansion and target type identification. When the distance between the shield and the target is smaller than the resolution distance determined by the laser pulse width, the method still has good point cloud expansion ability. -
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