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Open AccessArticle

Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

1
Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany
2
Otto von Guericke University, Faculty of Computer Science, 39106 Magdeburg, Germany
3
Forstliches Forschungs- und Kompetenzzentrum, ThüringenForst AöR, 99867 Gotha, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2788; https://doi.org/10.3390/rs11232788
Received: 29 October 2019 / Revised: 21 November 2019 / Accepted: 22 November 2019 / Published: 26 November 2019
In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75). View Full-Text
Keywords: hyperspectral imaging; tree species; multiple classifier fusion; convolutional neural network; random forest; rotation forest hyperspectral imaging; tree species; multiple classifier fusion; convolutional neural network; random forest; rotation forest
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Knauer, U.; von Rekowski, C.S.; Stecklina, M.; Krokotsch, T.; Pham Minh, T.; Hauffe, V.; Kilias, D.; Ehrhardt, I.; Sagischewski, H.; Chmara, S.; Seiffert, U. Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers. Remote Sens. 2019, 11, 2788.

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