{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:47:04Z","timestamp":1772725624248,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T00:00:00Z","timestamp":1574726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministerium f\u00fcr Umwelt, Landwirtschaft und Energie Sachsen-Anhalt","award":["A02\/2014"],"award-info":[{"award-number":["A02\/2014"]}]},{"name":"Ministerium f\u00fcr Umwelt, Landwirtschaft und Energie Sachsen-Anhalt","award":["A02\/2016"],"award-info":[{"award-number":["A02\/2016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +\/\u2212 0.086. The classification performance increased to an accuracy of 0.78 +\/\u2212 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).<\/jats:p>","DOI":"10.3390\/rs11232788","type":"journal-article","created":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T10:57:27Z","timestamp":1574765847000},"page":"2788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4423-1164","authenticated-orcid":false,"given":"Uwe","family":"Knauer","sequence":"first","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany"}]},{"given":"Cornelius Styp","family":"von Rekowski","sequence":"additional","affiliation":[{"name":"Otto von Guericke University, Faculty of Computer Science, 39106 Magdeburg, Germany"}]},{"given":"Marianne","family":"Stecklina","sequence":"additional","affiliation":[{"name":"Otto von Guericke University, Faculty of Computer Science, 39106 Magdeburg, Germany"}]},{"given":"Tilman","family":"Krokotsch","sequence":"additional","affiliation":[{"name":"Otto von Guericke University, Faculty of Computer Science, 39106 Magdeburg, Germany"}]},{"given":"Tuan","family":"Pham Minh","sequence":"additional","affiliation":[{"name":"Otto von Guericke University, Faculty of Computer Science, 39106 Magdeburg, Germany"}]},{"given":"Viola","family":"Hauffe","sequence":"additional","affiliation":[{"name":"Otto von Guericke University, Faculty of Computer Science, 39106 Magdeburg, Germany"}]},{"given":"David","family":"Kilias","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany"}]},{"given":"Ina","family":"Ehrhardt","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany"}]},{"given":"Herbert","family":"Sagischewski","sequence":"additional","affiliation":[{"name":"Forstliches Forschungs- und Kompetenzzentrum, Th\u00fcringenForst A\u00f6R, 99867 Gotha, Germany"}]},{"given":"Sergej","family":"Chmara","sequence":"additional","affiliation":[{"name":"Forstliches Forschungs- und Kompetenzzentrum, Th\u00fcringenForst A\u00f6R, 99867 Gotha, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6043-7947","authenticated-orcid":false,"given":"Udo","family":"Seiffert","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral Remote Sensing Data Analysis and Future Challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. 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