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dc.contributor.authorVillalón-Turrubiates, Iván E.
dc.contributor.authorLlovera-Torres, María J.
dc.date.accessioned2019-01-10T17:41:16Z
dc.date.accessioned2023-03-21T20:43:00Z
dc.date.available2019-01-10T17:41:16Z
dc.date.available2023-03-21T20:43:00Z
dc.date.issued2018-10
dc.identifier.citationIván E. Villalón-Turrubiates y María J. Llovera-Torres, “Enhanced Classification Model for Multispectral Observations from the Earth”, en ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: ISPRS TC IV Symposium 2018 – 3D Spatial Information Science, The Engine of Change, Delft, Países Bajos, 2018, pp. 221-226.es
dc.identifier.issn2194-9042
dc.identifier.urihttps://hdl.handle.net/20.500.12032/75039
dc.descriptionThe image classification procedure to identify remote sensing signatures from a particular geographical region can be performed with an identification model that has the ability to use large datasets to reach an accurate result. This novel methodology is referred to as the Statistical Enhanced Classification algorithm, which has been developed to employ multispectral images based in the statistical supervised learning theory and can be used for applications in environmental monitoring and analysis. This paper presents the performance study of the proposed methodology using both, multispectral synthetic images and multispectral remote sensing images. The obtained results are accurate due to the use of several spectral bands, the use of statistics such as mean and standard deviation for the training classes and for the pixel neighborhood, which provides more robust information, and the decision-making rule that has the ability to decide if the pixel is not belonging to a predefined class, which leads to an accurate decision model.es
dc.language.isoenges
dc.publisherISPRS TC IVes
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectIdentificationes
dc.subjectImage Classificationes
dc.subjectImage Processinges
dc.subjectRemote Sensinges
dc.titleEnhanced Classification Model for Multispectral Observations from the Earthes
dc.typeinfo:eu-repo/semantics/conferencePaperes


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