Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9535
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Main Title: A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data
Author(s): Nguyen, Hoang Minh
Demir, Begüm
Dalponte, Michele
Type: Article
Language Code: en
Abstract: 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.
URI: https://depositonce.tu-berlin.de/handle/11303/10609
http://dx.doi.org/10.14279/depositonce-9535
Issue Date: 9-Dec-2019
Date Available: 16-Jan-2020
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Subject(s): LiDAR
tree species classification
support vector machines
weighed support vector machines
Sponsor/Funder: EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarth
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Remote Sensing
Publisher: MDPI
Publisher Place: Basel
Volume: 11
Issue: 24
Article Number: 2948
Publisher DOI: 10.3390/rs11242948
EISSN: 2072-4292
Appears in Collections:FG Remote Sensing Image Analysis Group » Publications

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