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Open AccessArticle

Optimizing Multiple Kernel Learning for the Classification of UAV Data

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands
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Author to whom correspondence should be addressed.
Academic Editors: Farid Melgani, Xiaofeng Li and Prasad S. Thenkabail
Remote Sens. 2016, 8(12), 1025; https://doi.org/10.3390/rs8121025
Received: 26 October 2016 / Revised: 8 December 2016 / Accepted: 9 December 2016 / Published: 16 December 2016
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
Unmanned Aerial Vehicles (UAVs) are capable of providing high-quality orthoimagery and 3D information in the form of point clouds at a relatively low cost. Their increasing popularity stresses the necessity of understanding which algorithms are especially suited for processing the data obtained from UAVs. The features that are extracted from the point cloud and imagery have different statistical characteristics and can be considered as heterogeneous, which motivates the use of Multiple Kernel Learning (MKL) for classification problems. In this paper, we illustrate the utility of applying MKL for the classification of heterogeneous features obtained from UAV data through a case study of an informal settlement in Kigali, Rwanda. Results indicate that MKL can achieve a classification accuracy of 90.6%, a 5.2% increase over a standard single-kernel Support Vector Machine (SVM). A comparison of seven MKL methods indicates that linearly-weighted kernel combinations based on simple heuristics are competitive with respect to computationally-complex, non-linear kernel combination methods. We further underline the importance of utilizing appropriate feature grouping strategies for MKL, which has not been directly addressed in the literature, and we propose a novel, automated feature grouping method that achieves a high classification accuracy for various MKL methods. View Full-Text
Keywords: Unmanned Aerial Vehicles (UAVs); Support Vector Machines (SVMs); Multiple Kernel Learning (MKL); informal settlements; image classification Unmanned Aerial Vehicles (UAVs); Support Vector Machines (SVMs); Multiple Kernel Learning (MKL); informal settlements; image classification
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MDPI and ACS Style

Gevaert, C.M.; Persello, C.; Vosselman, G. Optimizing Multiple Kernel Learning for the Classification of UAV Data. Remote Sens. 2016, 8, 1025. https://doi.org/10.3390/rs8121025

AMA Style

Gevaert CM, Persello C, Vosselman G. Optimizing Multiple Kernel Learning for the Classification of UAV Data. Remote Sensing. 2016; 8(12):1025. https://doi.org/10.3390/rs8121025

Chicago/Turabian Style

Gevaert, Caroline M.; Persello, Claudio; Vosselman, George. 2016. "Optimizing Multiple Kernel Learning for the Classification of UAV Data" Remote Sens. 8, no. 12: 1025. https://doi.org/10.3390/rs8121025

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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