Pure and Applied Geophysics

, Volume 165, Issue 8, pp 1555-1575

First online:

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

  • Daniel GómezAffiliated withEscuela de Estadística, Complutense University, Madrid Email author 
  • , Javier MonteroAffiliated withFaculty of Mathematics, Complutense University
  • , Gregory BigingAffiliated withDepartment of Environmental Science, Policy and Management, University of California

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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.


Classification fuzzy sets fuzzy partition multicriteria analysis image segmentation