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Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics



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Gómez, D. y Montero, Javier y Binging, Gregory (2008) Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics. Pure and Applied Geophysics, 165 (8). pp. 1555-1575. ISSN 0033-4553

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URL Oficial: http://www.springerlink.com/content/f0673477072v770w/

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This paper puts together some techniques that have been previously developed by the authors,but separately, relative to fuzzy classification within a remote sensing setting. Considering that each image can be represented as a graph that defines proximity between pixels, certain distances between the characteristic of contiguous pixels are defined on such a graph, so a segmentation of the image into homogeneous regions can be produced by means of a particular algorithm. Such a segmentation can be then introduced as information, previously to any classification procedure, with an expected significative improvement. In particular, we consider specific measures in order to quantify such an improvement. This approach is being illustrated with its application into a particular land surface problem.

Tipo de documento:Artículo
Palabras clave:Classification; Fuzzy sets, fuzzy partition; Multicriteria analysis; Image segmentation.
Materias:Ciencias > Informática > Inteligencia artificial
Código ID:16189
Depositado:10 Sep 2012 11:52
Última Modificación:20 Abr 2016 13:27

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