Abstract

Light Detection and Ranging (LiDAR) produces 3D point clouds that describe ground objects, and has been used to make object interpretation in many cases. However, traditional LiDAR only records discrete echo signals and provides limited feature parameters of point clouds, while full-waveform LiDAR (FWL) records the backscattered echo in the form of a waveform, which provides more echo information. With the development of machine learning, support vector machine (SVM) is one of the commonly used classifiers to deal with high dimensional data via small amount of samples. Ensemble learning, which combines a set of base classifiers to determine the output result, is presented and SVM ensemble is used to improve the discrimination ability, owing to small differences in features between different types of data. In addition, previous kernel functions of SVM usually cause under-fitting or over-fitting that decreases the generalization performance. Hence, a series of kernel functions based on wavelet analysis are used to construct different wavelet SVMs (WSVMs) that improve the heterogeneity of ensemble system. Meanwhile, the parameters of SVM have a significant influence on the classification result. Therefore, in this paper, FWL point clouds are classified by WSVM ensemble and particle swarm optimization is used to find the optimal parameters of WSVM. Experimental results illustrate that the proposed method is robust and effective, and it is applicable to some practical work.

Highlights

  • The remote sensing technique is based on the sensor recorded energy that is reflected or emitted from the Earth’s surface [1]

  • In this paper, full-waveform LiDAR (FWL) point clouds are classified by wavelet SVMs (WSVMs) ensemble and particle swarm optimization is used to find the optimal parameters of WSVM

  • The data used in this paper were acquired by airborne Light Detection and Ranging (LiDAR) system ALS60, in 2009, and in the form of Las 1.3

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Summary

Introduction

The remote sensing technique is based on the sensor recorded energy that is reflected or emitted from the Earth’s surface [1]. Remote sensing can acquire information about ground objects without physical contact and can be divided into two categories: passive and active. Passive remote sensing data mostly exist in the form of spectral images, which makes it difficult for them to describe. Light Detection and Ranging (LiDAR) is a type of active remote sensing technique. LiDAR point clouds are used to produce elevation data, such as the Digital Elevation Model (DEM) and Digital Surface. Model (DSM), which is typically employed as ancillary information to assist passive remote sensed data in classification [3,4,5,6]. With the hardware equipment of LiDAR, more and more features are extracted

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