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Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines

Löw, Fabian and Ulrich, Michel and Dech, Stefan and Conrad, Christopher (2013) Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines. ISPRS Journal of Photogrammetry and Remote Sensing, 85, pp. 102-119. Elsevier. DOI: 10.1016/j.isprsjprs.2013.08.007 ISSN 0924-2716

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Abstract

Crop mapping is one major component of agricultural resource monitoring using remote sensing. Yield orwater demand modeling requires that both, the total surface that is cultivated and the accurate distributionof crops, respectively is known. Map quality is crucial and influences the model outputs. Althoughthe use of multi-spectral time series data in crop mapping has been acknowledged, the potentially highdimensionality of the input data remains an issue. In this study Support Vector Machines (SVM) are usedfor crop classification in irrigated landscapes at the object-level. Input to the classifications is 71 multiseasonalspectral and geostatistical features computed from RapidEye time series. The random forest (RF)feature importance score was used to select a subset of features that achieved optimal accuracies. Therelationship between the hard result accuracy and the soft output from the SVM is investigated byemploying two measures of uncertainty, the maximum a posteriori probability and the alpha quadraticentropy. Specifically the effect of feature selection on map uncertainty is investigated by looking at thesoft outputs of the SVM, in addition to classical accuracy metrics. Overall the SVMs applied to the reduced feature subspaces that were composed of the most informative multi-seasonal features led to a clearincrease in classification accuracy up to 4.3%, and to a significant decline in thematic uncertainty. SVMwas shown to be affected by feature space size and could benefit from RF-based feature selection. Uncertaintymeasures from SVM are an informative source of information on the spatial distribution of error inthe crop maps.

Item URL in elib:https://elib.dlr.de/87477/
Document Type:Article
Title:Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Löw, Fabianfabian.loew (at) uni-wuerzburg.deUNSPECIFIED
Ulrich, Michelmichel (at) ph-heidelberg.deUNSPECIFIED
Dech, Stefanstefan.dech (at) dlr.deUNSPECIFIED
Conrad, Christopherchristopher.conrad (at) uni-wuerzburg.deUNSPECIFIED
Date:2013
Journal or Publication Title:ISPRS Journal of Photogrammetry and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:85
DOI :10.1016/j.isprsjprs.2013.08.007
Page Range:pp. 102-119
Publisher:Elsevier
ISSN:0924-2716
Status:Published
Keywords:Crop classification, feature selection, map uncertainty, random forest, RapidEye, support vector machines
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:22 Jan 2014 21:13
Last Modified:06 Sep 2019 15:28

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