大光斑LiDAR全波形数据小波变换的高斯递进分解

高斯分解是波形激光雷达数据预处理的常用方法, 但在应用于大光斑全波形激光雷达数据中的叠加波时却难以发挥作用, 为此提出一种基于小波变换的高斯递进波形分解方法.首先, 利用小波变换多尺度分析特性检测出目标地物并准确估算组分特征参数, 进而建立高斯模型优化特征参数；然后通过拟合精度指标, 判断是否需要添加新组分进行逐级递进分解, 确定最终模型及其组分构成, 最终实现全波形激光雷达数据的波形分解.为了验证算法的有效性, 分别对实验数据使用本文算法和常用的基于拐点匹配的高斯分解法进行分析, 结果表明, 本文算法提取的目标数几乎是拐点匹配算法的2倍, 可以有效地从叠加波中检测出目标组分, 且拟合精度高于98%.

Abstract

Gaussian decomposition is a commonly used method for waveform analysis, which is a key post-processing step for the applications of large footprint LiDAR data. However, it usually fails to detect the overlapping pulses of large-footprint waveform data. Therefore, a Gaussian progressive decomposition method based on wavelet transform was proposed in this study to address this issue and applied to Ice, Cloud, and land Elevation Satellite / Geoscience Laser Altimeter System (ICESat/GLAS) data. The new proposed method mainly consists of three key steps. First, the wavelet transform was adopted to detect the target features and estimate the component feature parameters, then the Gaussian model was established to optimize the feature parameters. Second, a new component was added if the fitting accuracy didn’t meet the requirements. Finally, waveform decomposition based on wavelet transform was completed until no more new components were added. Additionally, a comparison experiment between the new proposed method and the Gaussian decomposition method based on inflection point was also conducted to verify the reliability of the new proposed algorithm. Experiment results indicated that our new proposed algorithm can detect twice targets as many as the method based on inflection point, and effectively decompose the targets from overlapping waveforms due to high fitting accuracy of above 98%.

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杨学博, 王成, 习晓环, 田建林, 聂胜, 朱笑笑. 大光斑LiDAR全波形数据小波变换的高斯递进分解[J]. 红外与毫米波学报, 2017, 36(6): 749. YANG Xue-Bo, WANG Cheng, XI Xiao-Huan, TIAN Jian-Lin, NIE Sheng, ZHU Xiao-Xiao. Wavelet transform of Gaussian progressive decomposition method for full-waveform LiDAR data[J]. Journal of Infrared and Millimeter Waves, 2017, 36(6): 749.

大光斑LiDAR全波形数据小波变换的高斯递进分解

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