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Article: Object-based detection and classification of Vehicles from high-resolution aerial photography

TitleObject-based detection and classification of Vehicles from high-resolution aerial photography
Authors
Issue Date2009
Citation
Photogrammetric Engineering and Remote Sensing, 2009, v. 75, n. 7, p. 871-880 How to Cite?
AbstractVehicle counts and truck percentages are important input variables in both noise pollution and air quality models, but the acquisition of these variables through fixed-point methods can be expensive, labor-intensive, and provide incomplete spatial sampling. The increasing availability and decreasing cost of high spatial resolution imagery provides an opportunity to improve the descriptive ability of traffic volume analysis. This study describes an object-based classification technique to extract vehicle volumes and vehicle type distributions from aerial photos sampled throughout a large metropolitan area. We developed rules for optimizing segmentation parameters, and used feature space optimization to choose classification attributes and develop fuzzy-set memberships for classification. Vehicles were extracted from street areas with 91.8 percent accuracy. Furthermore, separation of vehicles into classes based on car, medium-sized truck, and buses/heavy truck definitions was achieved with 87.5 percent accuracy. We discuss implications of these results for traffic volume analysis and parameterization of existing noise and air pollution models, and suggest future work for traffic assessment using high-resolution remotely-sensed imagery. © 2009 American Society for Photogrammetry and Remote Sensing.
Persistent Identifierhttp://hdl.handle.net/10722/296653
ISSN
2023 Impact Factor: 1.0
2023 SCImago Journal Rankings: 0.309
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHolt, Ashley C.-
dc.contributor.authorSeto, Edmund Y.W.-
dc.contributor.authorRivard, Tom-
dc.contributor.authorGong, Peng-
dc.date.accessioned2021-02-25T15:16:22Z-
dc.date.available2021-02-25T15:16:22Z-
dc.date.issued2009-
dc.identifier.citationPhotogrammetric Engineering and Remote Sensing, 2009, v. 75, n. 7, p. 871-880-
dc.identifier.issn0099-1112-
dc.identifier.urihttp://hdl.handle.net/10722/296653-
dc.description.abstractVehicle counts and truck percentages are important input variables in both noise pollution and air quality models, but the acquisition of these variables through fixed-point methods can be expensive, labor-intensive, and provide incomplete spatial sampling. The increasing availability and decreasing cost of high spatial resolution imagery provides an opportunity to improve the descriptive ability of traffic volume analysis. This study describes an object-based classification technique to extract vehicle volumes and vehicle type distributions from aerial photos sampled throughout a large metropolitan area. We developed rules for optimizing segmentation parameters, and used feature space optimization to choose classification attributes and develop fuzzy-set memberships for classification. Vehicles were extracted from street areas with 91.8 percent accuracy. Furthermore, separation of vehicles into classes based on car, medium-sized truck, and buses/heavy truck definitions was achieved with 87.5 percent accuracy. We discuss implications of these results for traffic volume analysis and parameterization of existing noise and air pollution models, and suggest future work for traffic assessment using high-resolution remotely-sensed imagery. © 2009 American Society for Photogrammetry and Remote Sensing.-
dc.languageeng-
dc.relation.ispartofPhotogrammetric Engineering and Remote Sensing-
dc.titleObject-based detection and classification of Vehicles from high-resolution aerial photography-
dc.typeArticle-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.14358/PERS.75.7.871-
dc.identifier.scopuseid_2-s2.0-68649090261-
dc.identifier.volume75-
dc.identifier.issue7-
dc.identifier.spage871-
dc.identifier.epage880-
dc.identifier.isiWOS:000268465100012-
dc.identifier.issnl0099-1112-

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