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dc.contributor.author
Xiao, Changlin
dc.contributor.author
Qin, Rongjun
dc.contributor.author
Ling, Xiao
dc.date.accessioned
2020-03-18T12:57:40Z
dc.date.available
2020-03-15T02:34:03Z
dc.date.available
2020-03-18T12:57:40Z
dc.date.issued
2020-02
dc.identifier.issn
2072-4292
dc.identifier.other
10.3390/rs12030390
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/404926
dc.identifier.doi
10.3929/ethz-b-000404926
dc.description.abstract
Land-cover classification on very high resolution data (decimetre-level) is a well-studied yet challenging problem in remote sensing data processing. Most of the existing works focus on using images with orthographic view or orthophotos with the associated digital surface models (DSMs). However, the use of the nowadays widely-available oblique images to support such a task is not sufficiently investigated. In the effort of identifying different land-cover classes, it is intuitive that information of side-views obtained from the oblique can be of great help, yet how this can be technically achieved is challenging due to the complex geometric association between the side and top views. We aim to address these challenges in this paper by proposing a framework with enhanced classification results, leveraging the use of orthophoto, digital surface models and oblique images. The proposed method contains a classic two-step of (1) feature extraction and (2) a classification approach, in which the key contribution is a feature extraction algorithm that performs simplified geometric association between top-view segments (from orthophoto) and side-view planes (from projected oblique images), and joint statistical feature extraction. Our experiment on five test sites showed that the side-view information could steadily improve the classification accuracy with both kinds of training samples (1.1% and 5.6% for evenly distributed and non-evenly distributed samples, separately). Additionally, by testing the classifier at a large and untrained site, adding side-view information showed a total of 26.2% accuracy improvement of the above-ground objects, which demonstrates the strong generalization ability of the side-view features.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
MDPI
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Land-cover classification
en_US
dc.subject
Side-view
en_US
dc.subject
Oblique image
en_US
dc.subject
Photogrammetry
en_US
dc.title
Urban Land-Cover Classification Using Side-View Information from Oblique Images
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-01-26
ethz.journal.title
Remote Sensing
ethz.journal.volume
12
en_US
ethz.journal.issue
3
en_US
ethz.journal.abbreviated
Remote Sens.
ethz.pages.start
390
en_US
ethz.size
18 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
Basel
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2020-03-15T02:34:10Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-03-18T12:57:52Z
ethz.rosetta.lastUpdated
2022-03-29T01:21:50Z
ethz.rosetta.versionExported
true
ethz.COinS
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