Identification of Agricultural Crop Types in Northern Israel using Multitemporal RapidEye Data

DC FieldValueLanguage
dc.contributor.authorBeyer, Florian
dc.contributor.authorJarmer, Thomas
dc.contributor.authorSiegmann, Bastian
dc.date.accessioned2021-12-23T15:59:28Z-
dc.date.available2021-12-23T15:59:28Z-
dc.date.issued2015
dc.identifier.issn14328364
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/3951-
dc.description.abstractAccurate land use / land cover classification (LU/LC) of agricultural crops still represents a major challenge for multispectral remote sensing. In order to obtain reliable classification accuracies on the basis of multispectral satellite data, merging crop classes in rather broad classes is often necessary. With regard to the rising availability and the improving spatial resolution of satellite data, multitemporal analyses become increasingly important for remote sensing investigations. For the separation of spectrally similar crops, multidate satellite images include different growth characteristics during the phenological period. The present study aims at investigating a way to perform highly accurate classifications with numerous agricultural classes using multitemporal Rapid Eye data. The Jeffries-Matusita separability (JM) was used for applying a pre-procedure in order to find the best multitemporal setting of all available images within one crop cycle, consisting of two cultivation periods P1 with 16 agricultural classes and P2 with 27 agricultural classes. Only one critical class pairing occurred for both PI and P2 taking into account the best multitemporal dataset. The maximum likelihood (ML) classifier and the support vector machine (SVM) were compared using the most suitable multitemporal images. Both algorithms achieved very high overall accuracies (OAA) of over 90%. SVM was slightly better with a classification accuracy of P1-OAA = 96.13% and P2-OAA = 94.01%. ML provided a result of OAA = 94.83% correctly classified pixels for P1 and OAA = 93.28% for P2. The processing time of ML, however, was significantly shorter compared to SVM, in fact by a factor of five.
dc.description.sponsorshipGerman Aerospace Center (DLR)Helmholtz AssociationGerman Aerospace Centre (DLR); RESA Science Team; Neustrelitz [597]; State of Lower-Saxony, Hannover, Germany [ZN2725 11-76251-99-20/11]; The authors want to thank the German Aerospace Center (DLR), RESA Science Team, Neustrelitz for the support by providing the satellite data of the RapidEye Science Archive (proposal no. 597). We also thank the staff of the DLR (Oberpfaffenhofen), namely THOMAS KRAUSS and PETER FISCHER, for the atmospheric correction using the generic processing chain CATENA. The joint research project ``Inference of Aerosol and Land Use Interactions from Remote Sensing Data'' (Aerosol-Land, ZN2725 11-76251-99-20/11) was financially supported by the State of Lower-Saxony, Hannover, Germany.
dc.language.isoen
dc.publisherE SCHWEIZERBARTSCHE VERLAGSBUCHHANDLUNG
dc.relation.ispartofPHOTOGRAMMETRIE FERNERKUNDUNG GEOINFORMATION
dc.subjectALGORITHMS
dc.subjectcrops
dc.subjectDISCRIMINATION
dc.subjectImaging Science & Photographic Technology
dc.subjectLAND-COVER CLASSIFICATION
dc.subjectLU/LC
dc.subjectMULTISPECTRAL IMAGES
dc.subjectmultitemporal classification
dc.subjectphenology
dc.subjectRapidEye
dc.subjectRemote Sensing
dc.subjectSENSED DATA
dc.subjectSEPARABILITY
dc.subjectSUPPORT VECTOR MACHINES
dc.subjectTM IMAGERY
dc.subjectTRAINING DATA
dc.subjectVEGETATION
dc.titleIdentification of Agricultural Crop Types in Northern Israel using Multitemporal RapidEye Data
dc.typejournal article
dc.identifier.doi10.1127/pfg/2015/0249
dc.identifier.isiISI:000350707700003
dc.description.issue1
dc.description.startpage21
dc.description.endpage32
dc.contributor.orcid0000-0002-9203-320X
dc.contributor.orcid0000-0002-9203-320X
dc.contributor.researcheridF-6117-2019
dc.contributor.researcheridAAV-6509-2020
dc.publisher.placeNAEGELE U OBERMILLER, SCIENCE PUBLISHERS, JOHANNESSTRASSE 3A, D 70176 STUTTGART, GERMANY
dcterms.isPartOf.abbreviationPhotogramm. Fernerkund. Geoinf.
crisitem.author.deptFB 06 - Mathematik/Informatik-
crisitem.author.deptidfb06-
crisitem.author.orcid0000-0002-4652-1640-
crisitem.author.parentorgUniversität Osnabrück-
crisitem.author.netidJaTh054-
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