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Fusion of Multispectral LiDAR, Hyperspectral, and RGB Data for Urban Land Cover Classification

Hänsch, Ronny and Hellwich, Olaf (2020) Fusion of Multispectral LiDAR, Hyperspectral, and RGB Data for Urban Land Cover Classification. IEEE Geoscience and Remote Sensing Letters, pp. 1-5. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/lgrs.2020.2972955. ISSN 1545-598X.

Full text not available from this repository.

Abstract

With the increasing importance of monitoring urban areas, the question arises which sensors are best suited to solve the corresponding challenges. This letter proposes novel node tests within the random forest (RF) framework, which allows them to apply them to optical RGB images, hyperspectral images, and light detection and ranging (LiDAR) data, either individually or in combination. This does not only allow to derive accurate classification results for many relevant urban classes without preprocessing or feature extraction but also provides insights into which sensor offers the most meaningful data to solve the given classification task. The achieved results on a public benchmark data set are superior to results obtained by deep learning approaches despite being based on only a fraction of training samples.

Item URL in elib:https://elib.dlr.de/139675/
Document Type:Article
Title:Fusion of Multispectral LiDAR, Hyperspectral, and RGB Data for Urban Land Cover Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hänsch, RonnyRonny.Haensch (at) dlr.dehttps://orcid.org/0000-0002-2936-6765
Hellwich, OlafUNSPECIFIEDUNSPECIFIED
Date:31 May 2020
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1109/lgrs.2020.2972955
Page Range:pp. 1-5
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:Published
Keywords:Classification, data fusion, HIS, multispectral light detection and ranging (LiDAR), random forest (RF)
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 - Aircraft SAR
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > SAR Technology
Deposited By: Hänsch, Ronny
Deposited On:16 Dec 2020 10:38
Last Modified:15 Jul 2021 16:26

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