dc.creatorVillalón-Turrubiates, Iván E.
dc.date2016-04-21T19:21:56Z
dc.date2016-04-21T19:21:56Z
dc.date2008
dc.date.accessioned2023-07-21T21:58:08Z
dc.date.available2023-07-21T21:58:08Z
dc.identifierIvan E. Villalon-Turrubiates, “Weighted Pixel Statistics for Multispectral Image Classification of Remote Sensing Signatures: Performance Study”, en Proceedings of the 5rd IEEE International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), Ciudad de México, 2008, pp. 534-539.
dc.identifier978-1-4244-2498-6
dc.identifierhttp://hdl.handle.net/11117/3307
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7756323
dc.descriptionThe extraction of remote sensing signatures from a particular geographical region allows the generation of electronic signature maps, which are the basis to create a high- resolution collection atlas processed in continuous discrete time. This can be achieved using a new multispectral image classification approach based on pixel statistics for the class description. This is referred to as the Weighted Pixel Statistics Method. This paper explores the effectiveness of this novel approach developed for supervised segmentation and classification of remote sensing signatures, with a comparison with the traditional Weighted Order Statistics Method. The extraction of remote sensing signatures from real-world high- resolution environmental remote sensing imagery is reported to probe the efficiency of the developed technique.
dc.descriptionPrograma de Mejoramiento del Profesorado PROMEP
dc.descriptionUniversidad de Guadalajara
dc.formatapplication/pdf
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relationIEEE International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE);5rd
dc.rightshttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdf
dc.subjectImage Segmentation
dc.subjectImage Classification
dc.subjectRemote Sensing
dc.subjectStatistics
dc.titleWeighted Pixel Statistics for Multispectral Image Classification of Remote Sensing Signatures: Performance Study
dc.typeinfo:eu-repo/semantics/conferencePaper


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