A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data
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Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all’Adige (TN), Italy
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External Affairs Office, Hanoi University of Science and Technology, No.1 Dai Co Viet Street, Hanoi 100000, Vietnam
3
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 2948; https://doi.org/10.3390/rs11242948
Received: 9 October 2019
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Revised: 21 November 2019
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Accepted: 6 December 2019
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Published: 9 December 2019
(This article belongs to the Special Issue Advances in Active Remote Sensing of Forests)
Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.
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Keywords:
LiDAR; tree species classification; support vector machines; weighed support vector machines
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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
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Externally hosted supplementary file 1
Doi: 10.5281/zenodo.3549595
Link: https://zenodo.org/record/3549595
Description: Datasets used in this study.
MDPI and ACS Style
Nguyen, H.M.; Demir, B.; Dalponte, M. A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data. Remote Sens. 2019, 11, 2948. https://doi.org/10.3390/rs11242948
AMA Style
Nguyen HM, Demir B, Dalponte M. A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data. Remote Sensing. 2019; 11(24):2948. https://doi.org/10.3390/rs11242948
Chicago/Turabian StyleNguyen, Hoang M., Begüm Demir, and Michele Dalponte. 2019. "A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data" Remote Sensing 11, no. 24: 2948. https://doi.org/10.3390/rs11242948
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