基于原始探测光子泊松分布特性的直方图去噪算法研究

Research on a Histogram Denoising Algorithm Based on Poisson Distribution Characteristics of Original Detected Photons

ICESat-2作为新一代多波束激光测高卫星采用了光子计数体制,其探测数据中存在大量噪声,造成光子数据在轨处理和传输时面临巨大挑战。为了在轨高效地对原始探测数据进行去噪处理,以降低星地传输数据量,本文设计了一种基于原始探测光子泊松分布特性的直方图去噪算法。该算法分为光子点云的垂直直方图化和倾斜直方图化。首先,根据光子传输距离将点云数据段划分为二维格网形成垂直直方图,利用直方图箱光子数的均值和标准差计算信噪分离阈值,并对信号光子赋予低、中和高置信度标签来表征信号可靠性;其次,对识别的中、高置信度光子进行线性拟合获取坡度信息,将光子传输距离投影到沿坡面垂直的方向形成倾斜直方图,进行二次信号光子识别且合并置信度标签;最后,对噪声光子进行剔除以实现原始数据的压缩下传。同时,本文对比研究了基于密度的空间聚类应用(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)算法和对点排序以识别聚类结构(Ordering Points to Identify the Clustering Structure,OPTICS)算法性能。通过8种地类的ATL02数据开展测试实验,结果表明,DBSCAN算法会导致信号光子丢失,而OPTICS算法会产生虚假信号簇,相比之下,直方图算法有效避免了这些问题的产生,并且在实验中对不同地类具有较好的稳健性和适应性。在直方图算法中,垂直直方图算法对信号光子召回率R均为1,查准率P和调和F值平均在0.90以上,相较于垂直+倾斜直方图、DBSCAN和OPTICS的运行效率分别达到12倍、3473倍和1528倍以上,平均运行时间仅0.048 s,可高效实现数据去噪。垂直直方图算法去噪结果初步满足在轨处理效率要求(<0.25 s),本文有望为未来星载光子计数激光雷达数据的在轨去噪提供技术参考。

ICESat-2,a new-generation satellite for spaceborne lidar altimetry,adopts a multi-beam single-photon counting regime.The presence of a large number of noisy photons in its detection data is well known,and the large amount of photonic data poses a challenge to data transmission and processing due to the limited processor performance and satellite storage resources on board the satellite.Therefore,in order to efficiently denoise the raw detection data in orbit and reduce the data volume for transmission from space to ground,this study proposes a histogram denoising algorithm based on the Poisson distribution characteristics of the raw detection photons.The method includes creating vertical and skewed histograms of the photon point cloud.Firstly,the point cloud data segment is divided into a two-dimensional grid to form a vertical histogram based on the photon transmission distance.The thresholds for distinguishing signal and noise photons are calculated from the mean and standard deviation of the number of photons in the histogram box,and signal photons are assigned with low,medium,and high confidence labels to characterize signal reliability.This is the process of vertical histogramming.Secondly,the slope information is obtained by linearly fitting the medium-and high-confidence photons from the first step.The photon transmission distance is projected to the vertical direction along the slope to form a tilted histogram,and the signal is recognized twice,and the confidence labels are merged.Finally,the noise is eliminated to achieve the purpose of compressing the original data for downlinking.This study also compares the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algorithm and Ordering Points to Identify the Clustering Structure(OPTICS)algorithm to evaluate the performance of the histogram denoising algorithm.Experiments were conducted using ATL02 data for eight surface types.The results show that DBSCAN and OPTICS are not suitable for data denoising in urban areas,with F-values of 0.766 and 0.765,respectively.In contrast,the histogram algorithm is robust and adaptable to different landform types,with Fvalues of more than 0.90 in all the experimental areas.The DBSCAN algorithm results in the loss of signal photons,while the OPTICS algorithm produces spurious signal clusters.In contrast,these problems are effectively avoided due to the inclusion of signal rate constraints and horizontal to overlapping division histogram processing techniques in the histogram algorithm.Among the histogram algorithms,the vertical histogram algorithm achieves a signal photon recall(R)of 1,and the average precision(P)and F-value are more than 0.90.This improves the operation efficiency by 12 times,3473 times,and 1528 times compared with the vertical/skewed histogram,DBSCAN,and OPTICS algorithms,respectively.In addition,the average operation time of the vertical histogram algorithm is only 0.048 seconds,realizing efficient data denoising and compression.The denoising result of the vertical histogram algorithm initially meets the requirement of on-orbit processing efficiency(<0.25 seconds).This study can provide a technical reference for future on-orbit denoising of spaceborne photon-counting LiDAR data.