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Evaluating the sequential masking classification approach for improving crop discrimination in the Sudanian Savanna of West Africa

Forkour, Gerald and Conrad, Christopher and Thiel, Michael and Landmann, Tobias and Boubacar, Barry (2015) Evaluating the sequential masking classification approach for improving crop discrimination in the Sudanian Savanna of West Africa. Computers and Electronics in Agriculture, 118, pp. 380-389. Elsevier. DOI: 10.1016/j.compag.2015.09.020 ISSN 0168-1699

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Official URL: http://www.sciencedirect.com/science/article/pii/S0168169915002951

Abstract

Classification of remotely sensed data to reveal the spatial distribution of crop types has high potential for improving crop area estimates and supporting decision making. However, remotely sensed crop maps still demand improvements as e.g. variations in farm management practices (e.g. planting and harvesting dates), soil and other environmental factors cause overlaps in features available for classification and thus confusion in error matrices. In this study, a variant of the sequential masking classification technique was applied to multi-temporal optical and microwave remote sensing data (RapidEye, Landsat, TerraSAR-X) to improve the accuracy of crop discrimination in West Africa. This approach employs different sets of multi-temporal images to sequentially classify individual crop classes. The efficiency of the sequential masking approach was tested by comparing the results with that of a one-step classification, in which all crop classes were classified at the same time. Compared to the one-step classification, the sequential masking approach improved overall classification accuracies by between 6% and 9% while increments in the accuracy of individual crop classes were between 4% and 19%. The McNemar’s statistical test showed that the observed differences in accuracy of the two approaches were statistically significant at the 1% significance level. The findings of this study are important for crop mapping efforts in West Africa, where data and methodological constraints often hinder the accurate discrimination of crops.

Item URL in elib:https://elib.dlr.de/101074/
Document Type:Article
Title:Evaluating the sequential masking classification approach for improving crop discrimination in the Sudanian Savanna of West Africa
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Forkour, Geraldgerald.forkour (at) uni-wuerzburg.deUNSPECIFIED
Conrad, Christopherchristopher.conrad (at) uni-wuerzburg.deUNSPECIFIED
Thiel, MichaelMichael.Thiel (at) uni-wuerzburg.deUNSPECIFIED
Landmann, Tobiastlandmann (at) icipe.orgUNSPECIFIED
Boubacar, Barrybarry.b (at) wascal.orgUNSPECIFIED
Date:October 2015
Journal or Publication Title:Computers and Electronics in Agriculture
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:118
DOI :10.1016/j.compag.2015.09.020
Page Range:pp. 380-389
Publisher:Elsevier
ISSN:0168-1699
Status:Published
Keywords:Crop classification, Sequential masking, RapidEye, TerraSAR-X, West Africa
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:09 Mar 2016 13:35
Last Modified:08 Mar 2018 18:38

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