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dc.contributor.authorVillalón-Turrubiates, Iván E.
dc.date.accessioned2016-04-21T18:06:18Z
dc.date.accessioned2023-03-10T18:12:24Z
dc.date.available2016-04-21T18:06:18Z
dc.date.available2023-03-10T18:12:24Z
dc.date.issued2012-07
dc.identifier.citationIván E. Villalón-Turrubiates, “Distributed Land Use Classification with Improved Processing Time using High-Resolution Multispectral Data”, en Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): Remote Sensing for a Dynamic Earth, Múnich Alemania, 2012, pp. 6987-6990.es
dc.identifier.isbn978-1-4673-1160-1
dc.identifier.urihttps://hdl.handle.net/20.500.12032/71785
dc.descriptionImage classification techniques can be applied to a geographical image to obtain its land use characteristics. Multispectral and high-resolution remote sensing images are able to provide sufficient information for a more accurate segmentation, nevertheless, the classification algorithms applied to images with high spatial resolution requires many computational cycles, even for modern computers. This paper explores the effectiveness of a novel approach developed for supervised segmentation and classification of high-resolution remote sensing images using distributed processing techniques to improve the computational time required. This is referred to as the distributed pixel statistics method. Examples of remote sensing signatures extracted from real world and high-resolution remote sensing images are reported to probe the efficiency of the developed technique.es
dc.description.sponsorshipITESO, A.C.es
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.relation.ispartofseriesProceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): Remote Sensing for a Dynamic Earth;
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectImage Classificationes
dc.subjectRemote Sensinges
dc.subjectStatisticses
dc.subjectDistributed Processinges
dc.titleDistributed Land Use Classification with Improved Processing Time using High-Resolution Multispectral Dataes
dc.typeinfo:eu-repo/semantics/conferencePaperes


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