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

Fabian Löw; Gunther Schorcht; Ulrich Michel; Stefan Dech; Christopher Conrad

doi:10.1117/12.974588

25 October 2012 Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

Fabian Löw, Gunther Schorcht, Ulrich Michel, Stefan Dech, Christopher Conrad

Author Affiliations +

Proceedings Volume 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III; 85380R (2012) https://doi.org/10.1117/12.974588
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

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.

Citation Download Citation

Fabian Löw, Gunther Schorcht, Ulrich Michel, Stefan Dech, and Christopher Conrad "Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble", Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 85380R (25 October 2012); https://doi.org/10.1117/12.974588

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