Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers
by
Uwe Knauer 1,*
, Cornelius Styp von Rekowski 2, Marianne Stecklina 2, Tilman Krokotsch 2, Tuan Pham Minh 2, Viola Hauffe 2, David Kilias 1, Ina Ehrhardt 1, Herbert Sagischewski 3, Sergej Chmara 3 and Udo Seiffert 1
Uwe Knauer 1,*, Cornelius Styp von Rekowski 2, Marianne Stecklina 2, Tilman Krokotsch 2, Tuan Pham Minh 2, Viola Hauffe 2, David Kilias 1, Ina Ehrhardt 1, Herbert Sagischewski 3, Sergej Chmara 3 and Udo Seiffert 1
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
(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)
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).
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Keywords:
hyperspectral imaging; tree species; multiple classifier fusion; convolutional neural network; random forest; rotation forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
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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