单频机载激光测深海陆回波自动分类方法EI北大核心CSCD

海陆回波分类是机载激光测深中的一项波形预处理步骤,关系着后续信号检测和点云生成的精度。针对现有海陆回波分类方法不适用于单频机载激光测深系统且自动化程度不高的问题,本文提出一种单频机载激光测深海陆回波自动分类方法:首先,通过首末回波信号检测及点位计算获得回波的点云高程特征;然后,采用高程直方图拟合的方式确定平均水面位置,依据点云高程特征判定大部分回波的海陆属性,对余下的未定回波,仅保留其中的最强信号并统一处理为单信号回波,同时提取波形的信号特征和能量分布特征,依据点云高程特征的相似性自动建立训练样本集;最后,利用支持向量机分类器实现未定回波的分类。采用国产系统Mapper5000采集的实测数据进行试验,结果表明基于首末回波点云的初分类可快速、准确地对远离海陆交界处的回波进行分类,基于波形特征的未定回波分类可在自动建立的训练样本集支持下实现海陆交界处未定回波的高精度分类。与传统方法相比,本文方法无须近红外通道波形和人工样本的辅助就可以达到较高的分类精度,其中总体分类精度可达99.82%,海陆交界处分类精度可达91.59%。

Automatic sea-land waveform classification method for single-wavelength airborne LiDAR bathymetry

Sea-land waveform classification is a preprocessing procedure for airborne LiDAR bathymetry (ALB), which is related to the accuracy of subsequent signal detection and point cloud generation. However, the existing methods are not applicable for single-wavelength ALB systems and have low degree of automation. Thus, an automatic sea-land waveform classification method for single-wavelength ALB is proposed. The point cloud elevation features are firstly obtained by the detection of the first and last signals and the calculation of the point coordinates. With the mean water level elevation approximated by elevation histogram fitting, most of the sea and land waveforms are classified based on the elevation features. The remaining undefined waveforms are processed as single-signal waveforms with only the strongest signal retained. The signal features and energy distribution features are extracted from the waveforms, and a train sample set is automatically generated using the similarity of point cloud elevation features. The sea-land labels of the undefined waveforms are finally determined by a support vector machine classifier. The field data collected by a domestic ALB system (Mapper5000) are used to test the proposed method. The experimental results show that the initial classification based on the point cloud elevation features can quickly and accurately classify the waveforms away from the sea-land boundary, and the undefined waveform classification based on waveform features can automatically classify the waveforms closed to the sea-land boundary with the support of the self-established train sample set. Compared to the traditional methods, the proposed method can achieve high accuracy sea-land classification without the assistance of near-infrared channel and manual samples, and the overall accuracy and the accuracy of the areas closed to sea-land boundary reach 99.82% and 91.59%, respectively.