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



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Gómez, D. and Montero, Javier and 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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Official URL: http://www.springerlink.com/content/f0673477072v770w/


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.

Item Type:Article
Uncontrolled Keywords:Classification; Fuzzy sets, fuzzy partition; Multicriteria analysis; Image segmentation.
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:16189
Deposited On:10 Sep 2012 11:52
Last Modified:25 Jun 2018 07:40

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