RT Journal Article
T1 Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics
A1 Gómez González, Daniel
A1 Montero De Juan, Francisco Javier
A1 Binging, Gregory
AB 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.
PB Birkhäuser
SN 0033-4553
YR 2008
FD 2008
LK https://hdl.handle.net/20.500.14352/50078
UL https://hdl.handle.net/20.500.14352/50078
LA eng
NO Gómez, D., Montero, J., Biging, G.: Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics. Pure appl. geophys. 165, 1555-1575 (2008). https://doi.org/10.1007/s00024-004-0389-6
NO TIN2006 - 06190
DS Docta Complutense
RD 20 dic 2025