Next Article in Journal
Dryland Vegetation Functional Response to Altered Rainfall Amounts and Variability Derived from Satellite Time Series Data
Next Article in Special Issue
First Results of a Tandem Terrestrial-Unmanned Aerial mapKITE System with Kinematic Ground Control Points for Corridor Mapping
Previous Article in Journal
Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution
Previous Article in Special Issue
Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland
Article Menu
Issue 12 (December) cover image

Export Article

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
Author to whom correspondence should be addressed.
Academic Editors: Farid Melgani, Xiaofeng Li and Prasad S. Thenkabail
Remote Sens. 2016, 8(12), 1025;
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)
PDF [3249 KB, uploaded 19 December 2016]


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

Graphical abstract

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 (CC BY 4.0).

Share & Cite This Article

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.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top