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Targeted Land Cover Classification

Marconcini, Mattia and Fernández-Prieto, Diego and Buchholz, Tim (2014) Targeted Land Cover Classification. IEEE Transactions on Geoscience and Remote Sensing, 52 (7), pp. 4173-4193. IEEE - Institute of Electrical and Electronics Engineers. ISSN 0196-2892

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Official URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6605596

Abstract

This paper addresses a specific typology of land cover classification problems, hereinafter referred to as “targeted land cover classification”, where the objective is the identification of only one or few specific “targeted” land cover classes of interest, disregarding all the other potential classes present in the area under analysis. Such a challenging problem, which is common to a variety of operational information services and applications (e.g., agriculture, forestry, spatial planning, ecosystem monitoring, disaster management, habitat mapping, etc.), can be effectively solved by traditional supervised classification techniques provided that an exhaustive ground truth is available for all the land cover classes present in the region of interest. Such a requirement is seldom satisfied and presents several practical drawbacks and limitations, both in terms of time and economic cost that may render this task difficult to achieve in most real-life cases. However, the possibility to perform an effective targeted classification using only ground-truth samples for the class(es) of interest (hence avoiding the burden and cost associated with the collection of a full and exhaustive ground-truth information) would represent a significant advantage. In this paper, we present a novel technique capable of identifying specific land cover classes of interest by exploiting the ground truth only available for these targeted classes, while providing accuracies comparable to those of traditional fully-supervised methods. The proposed technique jointly exploits both the unlabeled samples of the image under investigation and the training samples only available for the targeted classes. In particular, the Expectation-Maximization (EM) algorithm and Markov random fields (MRF) are employed to estimate the probability density function of both the class(es) of interest and the unknown class representing the merger of all the unknown land cover classes characterizing the study area for which no ground-truth information is available. An extensive experimental analysis and cross-comparisons with both fully-supervised support vector machines (SVM) and ensembles of multiple one-class support vector data description (SVDD) classifiers on different datasets confirmed the effectiveness and the reliability of the proposed technique.

Item URL in elib:https://elib.dlr.de/83388/
Document Type:Article
Title:Targeted Land Cover Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Marconcini, MattiaMattia.Marconcini (at) dlr.deUNSPECIFIED
Fernández-Prieto, DiegoDiego.Fernandez (at) esa.intUNSPECIFIED
Buchholz, Timtim.buchholz1978 (at) googlemail.comUNSPECIFIED
Date:2014
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:52
Page Range:pp. 4173-4193
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:Land cover classification, one-class classifiers, expectation maximization, Markov random fields, targeted land cover classification, agriculture
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
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:17 Jul 2013 13:19
Last Modified:08 Mar 2018 18:34

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