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dc.contributor.authorVillalón-Turrubiates, Iván E.
dc.date.accessioned2017-09-15T19:05:28Z
dc.date.accessioned2023-03-10T18:02:22Z
dc.date.available2017-09-15T19:05:28Z
dc.date.available2023-03-10T18:02:22Z
dc.date.issued2017-07
dc.identifier.citationIván E. Villalón-Turrubiates, Identification Model for Large Remote Sensing Datasets Applied to Environmental Analysis within Mexico”, in Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): International Cooperation for Global Awareness, Fort Worth Texas EE.UU., 2017, pp. 3019-3022.es
dc.identifier.isbn978-1-5090-4951-6
dc.identifier.urihttps://hdl.handle.net/20.500.12032/71152
dc.descriptionThe classification procedure to identify remote sensing signatures from a particular geographical region can be achieved using an accurate identification model that is based on multispectral data and uses pixel statistics for the class description. This methodology is referred to as the Multispectral Identification Model. This paper presents this particular methodology applied to large remote sensing datasets (multispectral images obtained from the SPOT-5 satellite sensors) with the objective to perform environmental and land use analysis for regions within Mexico, taking advantage of high-performance computing techniques to improve the processing time and computational load. The results obtained uses real multispectral scenes (high- resolution optical images) to probe the efficiency of the classification technique.es
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.relation.ispartofseries2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): International Cooperation for Global Awareness;
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectImage Classificationes
dc.subjectImage Processinges
dc.subjectMultispectrales
dc.subjectRemote Sensinges
dc.titleIdentification Model for Large Remote Sensing Datasets Applied to Environmental Analysis within Mexicoes
dc.typeinfo:eu-repo/semantics/conferencePaperes


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