Performance Evaluation of a Multispectral Classificator that Employs High-Performance Computing Techniques

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dc.contributor.authorVillalón-Turrubiates, Iván E.
dc.date.accessioned2016-08-02T16:43:25Z
dc.date.available2016-08-02T16:43:25Z
dc.date.issued2016-07
dc.descriptionThe classification procedure to identify remote sensing signatures from a particular geographical region can be achieved using an accurate image classification approach which is based on multispectral sets and uses pixel statistics for the class description, and it is referred to as the Multispectral Pixel Classification method. This paper presents a study of the performance that this approach provides for supervised segmentation and classification of sensed signatures for land use analysis and using high- performance computing techniques compared with traditional programming methodologies. The results obtained with this study uses real multispectral scenes obtained with remote sensing techniques (high-resolution optical images) to probe the efficiency of the classification technique.es
dc.description.sponsorshipITESO, A.C.es
dc.identifier.citationIvan E. Villalon-Turrubiates, “Performance Evaluation of a Multispectral Classificator that Employs High-Performance Computing Techniques”, en Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): Advancing the Understanding of Our Living Planet, Beijing China, 2016, pp. 3838-3841.es
dc.identifier.isbn978-1-5090-3332-4
dc.identifier.urihttp://hdl.handle.net/11117/3772
dc.language.isoenges
dc.publisherIEEEes
dc.relation.ispartofseriesProceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS): Advancing the Understanding of Our Living Planet;
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectImage Classificationes
dc.subjectRemote Sensinges
dc.subjectImage Processinges
dc.subjectMultispectrales
dc.titlePerformance Evaluation of a Multispectral Classificator that Employs High-Performance Computing Techniqueses
dc.typeinfo:eu-repo/semantics/conferencePaperes
rei.peerreviewedYeses
rei.revisor2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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