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, 2012-09-24 - 2012-09-27, Edinburgh, UK. doi: 10.1117/12.974588.
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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/ | ||||||||||||||||||||||||
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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: |
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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 | ||||||||||||||||||||||||
DOI: | 10.1117/12.974588 | ||||||||||||||||||||||||
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 Start Date: | 24 September 2012 | ||||||||||||||||||||||||
Event End Date: | 27 September 2012 | ||||||||||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||||||||||
HGF - Program: | Space | ||||||||||||||||||||||||
HGF - Program Themes: | Earth Observation | ||||||||||||||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||||||||||||||
DLR - Program: | R EO - Earth Observation | ||||||||||||||||||||||||
DLR - Research theme (Project): | R - Geoscientific remote sensing and GIS methods | ||||||||||||||||||||||||
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: | 24 Apr 2024 19:57 |
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