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Improved crop classification using multitemporal RapidEye data

Beyer, Florian and Jarmer, Thomas and Siegmann, Bastian and Fischer, Peter (2015) Improved crop classification using multitemporal RapidEye data. In: 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 2015, pp. 1-4. IEEE Xplore. Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 2015 8th International Workshop on the, 22.-24. Juli 2015, Annecy, Frankreich. DOI: 10.1109/Multi-Temp.2015.7245780 ISBN 978-1-4673-7119-3

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Official URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7245780&punumber%3D7235770%26filter%3DAND%28p_IS_Number%3A7245742%29%26pageNumber%3D2


Land Use/Land Cover (LU/LC) of agricultural areas derived from remotely sensed data still remains very challenging. With regard to the rising availability and the improving spatial resolution of satellite data, multitemporal analyses become increasingly important for remote sensing investigations. Even crops with similar spectral behaviour can be separated by adding spectral information of different phenological stages. Hence, the potential of multi-date RapidEye data for classifying numerous agricultural classes was investigated in this study. In an agricultural area in Northern Israel two complete crop cycles 2013 and 2014 with two cultivation periods each were investigated. In order to avoid a high number of classification runs, a pre-procedure was tested to get the multitemporal data set which provides best spectral separability. Therefore, Jeffries-Matusita (JM) measure was used in order to obtain the best multitemporal setting of all available images within one cultivation period. Eight classifiers were applied to compare the potential of separating crops. The three algorithms Maximum Likelihood (ML), Random Forest (RF) and Support Vector Machine (SVM) outperformed by far the other classifiers with Overall Accuracies higher than 90 %. The processing time of ML and RF, however, was significantly shorter compared to SVM, in fact by a factor of five to seven.

Item URL in elib:https://elib.dlr.de/98767/
Document Type:Conference or Workshop Item (Poster)
Title:Improved crop classification using multitemporal RapidEye data
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Beyer, Florianfbeyer (at) igf.uos.deUNSPECIFIED
Jarmer, Thomastjarmer (at) igf.uni-osnabrueck.deUNSPECIFIED
Siegmann, Bastianbsiegmann (at) igf.uos.deUNSPECIFIED
Fischer, PeterPeter.Fischer (at) dlr.deUNSPECIFIED
Date:July 2015
Journal or Publication Title:8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 2015
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/Multi-Temp.2015.7245780
Page Range:pp. 1-4
Publisher:IEEE Xplore
Keywords:crops;maximum likelihood estimation;remote sensing;support vector machines;vegetation mapping;JM;Jeffries-Matusita measure;LU-LC;ML;Northern Israel;RF;SVM;agricultural areas;agricultural classes;crop classification;crop cycles;cultivation period;cultivation periods;land use-land cover;maximum likelihood;multidate RapidEye data;multitemporal RapidEye data;multitemporal analyses;random forest;remote sensing investigations;remotely sensed data;satellite data;spatial resolution;support vector machine;Accuracy;Agriculture;Radio frequency;Remote sensing;Satellites;Soil;Support vector machines
Event Title:Analysis of Multitemporal Remote Sensing Images (Multi-Temp), 2015 8th International Workshop on the
Event Location:Annecy, Frankreich
Event Type:Workshop
Event Dates:22.-24. Juli 2015
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:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Fischer, Peter
Deposited On:22 Oct 2015 15:52
Last Modified:02 Feb 2017 17:27

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