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dc.contributor.author
Peña, José M.
dc.contributor.author
Gutiérrez, Pedro A.
dc.contributor.author
Hervás-Martínez, César
dc.contributor.author
Six, Johan
dc.contributor.author
Plant, Richard E.
dc.contributor.author
López-Granados, Francisca
dc.date.accessioned
2019-08-29T12:44:13Z
dc.date.available
2017-06-11T11:22:14Z
dc.date.available
2019-08-29T12:44:13Z
dc.date.issued
2014-05-30
dc.identifier.issn
2072-4292
dc.identifier.other
10.3390/rs6065019
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/87119
dc.identifier.doi
10.3929/ethz-b-000087119
dc.description.abstract
The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.
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/3.0/
dc.subject
Agriculture
en_US
dc.subject
ASTER satellite images
en_US
dc.subject
Object-oriented image analysis
en_US
dc.subject
Hierarchical classification
en_US
dc.subject
Neural networks
en_US
dc.title
Object-Based Image Classification of Summer Crops with Machine Learning Methods
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
ethz.journal.title
Remote Sensing
ethz.journal.volume
6
en_US
ethz.journal.issue
6
en_US
ethz.journal.abbreviated
Remote Sens.
ethz.pages.start
5019
en_US
ethz.pages.end
5041
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
010200880
ethz.publication.place
Basel
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02703 - Institut für Agrarwissenschaften / Institute of Agricultural Sciences::03982 - Six, Johan / Six, Johan
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02703 - Institut für Agrarwissenschaften / Institute of Agricultural Sciences::03982 - Six, Johan / Six, Johan
ethz.date.deposited
2017-06-11T11:24:03Z
ethz.source
ECIT
ethz.identifier.importid
imp593652217458555263
ethz.ecitpid
pub:137180
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-26T20:06:52Z
ethz.rosetta.lastUpdated
2022-03-28T23:33:27Z
ethz.rosetta.versionExported
true
ethz.COinS
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