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Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

Löw, Fabian and Schorcht, Gunther and Michel, U. and Dech, Stefan and Conrad, Christopher (2012) Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble. In: Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 85380R, pp. 1-11. SPIE Remote Sensing, 24. - 27. Sept. 2012, Edinburgh, UK.

Full text not available from this repository.

Official URL: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1387492

Abstract

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as user´s and producer´s accuracy.

Item URL in elib:https://elib.dlr.de/91639/
Document Type:Conference or Workshop Item (Speech)
Title:Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Löw, Fabianfabian.loew (at) uni-wuerzburg.deUNSPECIFIED
Schorcht, GuntherGunther.Schorcht (at) uni-wuerzburg.deUNSPECIFIED
Michel, U.UNSPECIFIEDUNSPECIFIED
Dech, Stefanstefan.dech (at) dlr.deUNSPECIFIED
Conrad, Christopherchristopher.conrad (at) uni-wuerzburg.deUNSPECIFIED
Date:2012
Journal or Publication Title:Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 85380R
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-11
Status:Published
Keywords:Ensemble classifier, feature selection, Hughes phenomenon, map uncertainty, random forest (RF), RapidEye, support vector machine (SVM)
Event Title:SPIE Remote Sensing
Event Location:Edinburgh, UK
Event Type:international Conference
Event Dates:24. - 27. Sept. 2012
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
German Remote Sensing Data Center > Leitungsbereich DFD
Deposited By: Wöhrl, Monika
Deposited On:12 Dec 2014 14:50
Last Modified:12 Dec 2014 14:50

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