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LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine

Ghamisi, Pedram and Höfle, Bernhard (2017) LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine. IEEE Geoscience and Remote Sensing Letters, 14 (5), pp. 659-663. IEEE - Institute of Electrical and Electronics Engineers. ISSN 1545-598X.

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Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7873288


This letter proposes a novel framework for the classification of LiDAR-derived features. In this context, several features are extracted directly from the LiDAR point cloud data using aggregated local point neighborhoods, including laser echo ratio, variance of point elevation, plane fitting residuals, and echo intensity. Additionally, the LiDAR Digital Surface Model (DSM) is input to our classification. Thus, both the LiDAR raster DSM and also rich geometric and also backscatter 3D point cloud information aggregated to images are considered in our workflow. These extracted features are characterized as base images to be fed to extinction profiles to model spatial and contextual information. Then, a composite kernel SVM is investigated to efficiently integrate the elevation and spatial information suitable for the LiDAR data. Results indicate that the proposed method can obtain high classification accuracy using LiDAR data alone (e.g., more than 86% overall accuracy on the benchmark Houston LiDAR data using the standard set of training and test samples on all 15 classes) in a short CPU processing time.

Item URL in elib:https://elib.dlr.de/111103/
Document Type:Article
Title:LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ghamisi, Pedramdlr-imf/tum-lmfUNSPECIFIED
Höfle, Bernharduniversität heidelbergUNSPECIFIED
Date:April 2017
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 659-663
EditorsEmailEditor's ORCID iD
Frery, Alejandro C.acfrery@gmail.comUNSPECIFIED
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Extended Multi-Extinction Profile, Composite Kernel SVM, LiDAR
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Ghamisi, Pedram
Deposited On:16 Feb 2017 10:40
Last Modified:31 Jul 2019 20:08

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