基于波形形态特征的单频机载激光雷达测深全波形数据分类  

Classification of full waveform data for monochromatic airborne LiDAR bathymetry based on waveform morphological features

作　　者：李莹莹 刘子维 张静坤 吴琳琳 纪雪 王明常[1] Li Yingying;Liu Ziwei;Zhang Jingkun;Wu Linlin;Ji Xue;Wang Mingchang(Academy of Earth Exploration Science and Technology,Jilin University,Changchun 130000,China;Academy of Urban Rail Transportation,Shanghai University of Engineering and Technology,Shanghai 201620,China;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;Technology Innovation Center for Ocean Telemetry,Ministry of Natural Resources,Qingdao 266061,China)

机构地区：[1]吉林大学地球探测科学与技术学院,吉林长春130000 [2]上海工程技术大学城市轨道交通学院,上海201620 [3]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079 [4]自然资源部海洋遥测技术创新中心,山东青岛266061

出　　处：《红外与激光工程》2023年第9期156-167,共12页Infrared and Laser Engineering

基　　金：武汉大学测绘遥感信息工程国家重点实验室开放基金项目(22S02);自然资源部海洋遥测技术创新中心开放基金项目(2022002);国家自然科学基金项目(22FAA01871,42301505,52305445);吉林省自然科学基金项目(20210101098JC)。

摘　　要：单频机载激光雷达测深系统凭借低成本、低负载、高采样率等优势成为大范围海岸带地形地貌探测的理想选择。然而如何解决单频局限,在不依赖辅助传感器情况下实现全波形数据的准确划分成为精确点位坐标解算的关键环节。目前基于全波形形态特征进行波形分类研究缺乏系统性评估分析和普遍性结论。该研究尝试从全波形空间形态入手,细化了波形类别(异常波形、过拟合波形、陆地波形、海面波形和测深波形),在已有波形特征基础上,系统分析了不同类别波形的形态特征差异,有针对性地提取了24维波形特征并基于随机森林特征选择和分类模型完成了各特征分类性能及最佳特征组合评估与定量分析。研究证明,包括相邻两点间振幅偏差、震荡主频等在内的6维特征组合对5种波形的分类效果最好,总体分类精度可达98.55%,Kappa系数为0.9820。为了验证特征的普适性,另外选取了一块实验区域进行验证,得到水陆分类的总体精度为96.81%。Objective Monochromatic airborne LiDAR bathymetry becomes considerably favorable for topography and geomorphology detection over coastal area by means of its low cost,low load and high sampling rate.However,addressing the limitation of single wavelength to realize the accurate division of full waveform data independently from auxiliary sensor becomes the critical part of coordinate calculation.Given the existing literatures,there is a lack of systematic evaluation analysis and general conclusions for waveform classification contraposing to full waveform morphological features.Methods In view of the latest development of waveform features extraction,refined waveform categories(anomalies,over-fitted,land,sea surface and bathymetry waveforms),24-dimensional waveform features are designed and calculated upon systematic analysis on morphological characteristics of different waveforms,and then their classification performance and optimal feature combination are evaluated and quantitatively analyzed utilizing random forest feature selection and classification model.Results and Discussions The results proved that the combination of 6-dimensional features(Fig.8-11),including deviation of amplitude between two adjacent points and oscillating main frequency,is the most effective in classifying five waveforms,with an overall classification accuracy of 98.55% and a Kappa coefficient of 0.9820(Fig.9-10,Fig.12,Tab.1).To verify the universality of the features,an additional experimental area was selected for validation and the overall accuracy of water and land classification was 96.81%(Fig.13).Conclusions To accurately identify waveforms,a systematic analysis was conducted to determine the morphological differences between different types of waveforms,and 24-dimensional feature parameters were extracted.After the optimal feature combination and classification performance evaluation,it was found that the 6-dimensional features of oscillating main frequency f,ratio of peak Rp,deviation of amplitude between two adjacent pointsΔA,

关 键 词：机载激光雷达测深 全波形 波形特征 波形分类 

分 类 号：TN958.98[电子电信—信号与信息处理]