Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19298
DC FieldValueLanguage
dc.contributor.authorAyhan, Bulent-
dc.contributor.authorKwan, Chiman-
dc.contributor.authorBudavari, Bence-
dc.contributor.authorKwan, Liyun-
dc.contributor.authorLu, Yan-
dc.contributor.authorPerez, Daniel-
dc.contributor.authorLi, Jiang-
dc.contributor.authorSkarlatos, Dimitrios-
dc.contributor.authorVlachos, Marinos-
dc.date.accessioned2020-10-29T08:53:09Z-
dc.date.available2020-10-29T08:53:09Z-
dc.date.issued2020-08-01-
dc.identifier.citationRemote Sensing, 2020, vol. 12, no. 15, articl. no. 2502en_US
dc.identifier.issn20724292-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/19298-
dc.description.abstractLand cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofRemote Sensingen_US
dc.rights© by the authors.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCNNen_US
dc.subjectDeep learningen_US
dc.subjectDeepLabV3+en_US
dc.subjectMachine learningen_US
dc.subjectNDVIen_US
dc.subjectVegetationen_US
dc.titleVegetation detection using deep learning and conventional methodsen_US
dc.typeArticleen_US
dc.collaborationApplied Research LLCen_US
dc.collaborationOld Dominion Universityen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.countryUnited Statesen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.3390/RS12152502en_US
dc.relation.issue15en_US
dc.relation.volume12en_US
cut.common.academicyear2019-2020en_US
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn2072-4292-
crisitem.journal.publisherMDPI-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-2732-4780-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
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