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Semi-supervised SVM for individual tree crown species classification

Dalponte, Michele and Ene, Liviu Theodor and Marconcini, Mattia and Gobakken, Terje and Naesset, Erik (2015) Semi-supervised SVM for individual tree crown species classification. ISPRS Journal of Photogrammetry and Remote Sensing, 110, pp. 77-87. Elsevier. doi: 10.1016/j.isprsjprs.2015.10.010. ISSN 0924-2716.

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Official URL: http://www.sciencedirect.com/science/article/pii/S0924271615002403

Abstract

In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi- supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quan- tify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of- the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

Item URL in elib:https://elib.dlr.de/100204/
Document Type:Article
Title:Semi-supervised SVM for individual tree crown species classification
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Dalponte, Michelemichele.dalponte (at) fmach.itUNSPECIFIED
Ene, Liviu Theodoreneliviu (at) gmail.comUNSPECIFIED
Marconcini, MattiaMattia.Marconcini (at) dlr.deUNSPECIFIED
Gobakken, Terjeterje.gobakken (at) nmbu.noUNSPECIFIED
Naesset, Erikerik.naesset (at) nmbu.noUNSPECIFIED
Date:29 November 2015
Journal or Publication Title:ISPRS Journal of Photogrammetry and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:110
DOI :10.1016/j.isprsjprs.2015.10.010
Page Range:pp. 77-87
Publisher:Elsevier
ISSN:0924-2716
Status:Published
Keywords:Tree species classification, Semi-supervised classification, Hyperspectral data, SVM, Individual tree crowns
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben Fernerkundung der Landoberfläche (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Marconcini, Mattia
Deposited On:02 Dec 2015 12:52
Last Modified:07 Oct 2019 11:27

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