Airborne Lidar Bathymetry (ALB) surveys are traditionally used for measuring depths in shallow nearshore and back-bay areas. In this paper, we present a novel ALB waveform processing procedure, namely bottom return residual analysis, for bottom characterization. Waveform features obtained from the bottom return residual analysis are used in a supervised classification approach, i.e. Support Vector Machine, to differentiate between: 1) sand and rock bottoms and subsequently, 2) fine and coarse sand bottoms. The classification procedure was tested on ALB survey data collected with an Optech SHOALS-1000T ALB system that covers a ~ 7 km² area within 1 km from shore in the western Gulf of Maine, USA. The bottom classification results, when compared to ground-truth measurements, indicate a 96% overall accuracy for sand and rock classification and 86% overall accuracy for fine and coarse sand classification. Results of ALB-based bottom classification are compared with interpretations of a multibeam echosounder acoustic backscatter mosaic collected from the survey area.

传统上，机载激光雷达测深（ALB）测量用于测量浅海近岸和后海湾地区的深度。在本文中，我们提出了一种新颖的ALB波形处理程序，即底部返回残差分析，用于底部特征分析。从底部回波残差分析获得的波形特征用于监督分类方法（即支持向量机）中，以区分：1）砂岩底部和岩石底部，以及2）细砂岩底部和粗砂岩底部。分类程序是通过使用Optech SHOALS-1000T ALB系统收集的ALB调查数据进行测试的，该系统覆盖了约7 km ²美国缅因州西部海湾，距海岸1公里以内的区域。与地面实况测量结果相比，底部分类结果表明，沙子和岩石分类的总体精度为96％，精细和粗砂分类的总体精度为86％。将基于ALB的底部分类的结果与从调查区域收集的声学背向散射马赛克下的多波束回声的解释进行比较。