Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics
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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.
- Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics
Pure and Applied Geophysics
Volume 165, Issue 8 , pp 1555-1575
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- SP Birkhäuser Verlag Basel
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- fuzzy sets
- fuzzy partition
- multicriteria analysis
- image segmentation
- Industry Sectors
- Author Affiliations
- 1. Escuela de Estadística, Complutense University, Madrid, Madrid, Spain
- 2. Faculty of Mathematics, Complutense University, Madrid, Spain
- 3. Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, U.S.A.