基于蝙蝠算法高斯锐化的星载全波形激光雷达回波处理技术研究
作者:
作者单位:

1.昆明理工大学理学院;2.舜宇光学科技(集团)有限公司

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Research on Spaceborne Full-Waveform Lidar Echo Processing Technology Based on Bat Algorithm Gaussian Sharpening
Author:
Affiliation:

1.Faculty of Science, Kunming University of Science and Technology;2.Sunny Optical Technology (Group) Company Limited

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    摘要:

    星载全波形激光雷达作为一种先进的遥感技术,因其能够记录详细的地形和植被信息而在多个领域得到了广泛应用。然而,星载全波形激光雷达数据在采集过程中会受到诸如暗电流、光电探测器性能、探测目标周边环境和背景光等因素的影响,导致原始波形信号中存在显著噪声,对提取有效回波信息进行目标反演分析造成干扰。针对现有经典滤波算法中普遍存在的波形幅值下降问题,提出了一种自适应的滤波补偿波形幅值方法,利用蝙蝠算法优化高斯锐化算子参数,将高斯锐化算子与滤波后的波形数据进行卷积来实现波形补偿,自适应迭代确保补偿效果达到最佳。针对GEDI(Global Ecosystem Dynamics Investigation)回波数据进行实验验证,将文中方法与多种滤波算法进行比较,经滤波后最高波峰幅值平均降低了9.0077,经过高斯锐化补偿后的波形与原始波形的最高波峰平均差值仅为0.0182,且平均信噪比由30.0235dB提升至33.2609dB,相对提升了10.78%。结果表明该方法结合滤波方法能够在去除噪声的同时,保留更多的原始波形特征信息,为进一步提取波形信息做地物参数反演和目标分类提供更为精确的数据,并且对多种滤波方法均有适用性。

    Abstract:

    Spaceborne full-waveform lidar, as an advanced remote sensing technology, has been widely applied in various fields due to its ability to record detailed terrain and vegetation information. However, the data from spaceborne full-waveform lidar can be affected by factors such as dark current, photodetector performance, the surrounding environment of the target being detected, and background light during the acquisition process. These factors introduce significant noise into the original waveform signals, interfering with the extraction of effective echo information for target inversion analysis. To address the common problem of waveform amplitude reduction in existing classical filtering algorithms, this paper proposes an adaptive filtering compensation method for waveform amplitude. By utilizing the bat algorithm to optimize Gaussian sharpening operator parameters and convolving the Gaussian sharpening operator with the filtered waveform data, waveform compensation is achieved through adaptive iteration to ensure optimal compensation effects. This paper conducts experimental verification on GEDI (Global Ecosystem Dynamics Investigation) echo data, comparing the proposed method with various filtering algorithms. After filtering, the highest peak amplitude was reduced by an average of 9.0077, while the difference between the highest peaks of the waveform after Gaussian sharpening compensation and the original waveform was only 0.0182 on average. Moreover, the average signal-to-noise ratio improved from 30.0235dB to 33.2609dB, representing a relative increase of 10.78%. The results indicate that this method, in conjunction with filtering methods, can remove noise while retaining more of the original waveform feature information. This provides more accurate data for further extraction of waveform information for geophysical parameter inversion and target classification and is applicable to a variety of filtering methods.

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  • 收稿日期:2024-12-05
  • 最后修改日期:2025-01-14
  • 录用日期:2025-01-20
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