Feature selection for airborne LiDAR data filtering: a mutual information method with Parzon window optimization. Issue 3 (2nd April 2020)

• Record Type: Journal Article
• Authors: Cai, Zhan; Ma, Hongchao; Zhang, Liang
• Is Part Of: GIScience & remote sensing. Volume 57:Issue 3(2020)
• Page Start: 323
• Page End: 337
• Publication Date: 2020-04-02
• Subjects: Airborne LiDAR -- feature selection -- filtering -- AdaBoost -- mRMR -- Parzen window; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2019.1695406
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: Filtering is one of the key steps for Digital Elevation Model (DEM) generation from airborne Light Detection and Ranging (LiDAR) data. Machine-learning-based filters have emerged as a class of filtering algorithms in recent years. Most existing studies mainly focus on feature generation due to limited available features a point cloud possesses. More than 30 features have been described in the existing literature. But most generated features are based on geometric information of points. Several redundant and irrelevant features may not necessarily improve the filtering accuracy. Hence, this paper proposes a feature-selection method using minimal-Redundancy-Maximal-Relevance (mRMR) combined with Parzen window optimization to deal with both discrete and continuous features. An optimal/suboptimal feature subset is constructed for machine-learning filters in various landscapes. Experimental results based on AdaBoost show that height-related features, particularly height itself, are of the greatest significance in both urban and rural scenes. Moreover, different subsets can be selected from the datasets of the two landscapes by our feature-selection strategy, which increases the data relevance for describing each geographical landscape. This study provides guidelines for the selection of optimal/suboptimal features for point cloud filtering based on machine-learning algorithms.