RT Journal Article
T1 Fuzzy sets in remote sensing classification
A1 Gómez González, Daniel
A1 Montero De Juan, Francisco Javier
AB Supervised classification in remote sensing is a very complex problem and involves steps of different nature, including a serious data preprocessing. The final objective can be stated in terms of a classification of isolated pixels between classes, which can be either previously known or not (for example, different land uses), but with no particular shape nither well defined borders. Hence, a fuzzy approach seems natural in order to capture the structure of the image. In this paper we stress that some useful tools for a fuzzy classification can be derived from fuzzy coloring procedures, to be extended in a second stage to the complete non visible spectrum. In fact, the image is considered here as a fuzzy graph defined on the set of pixels, taking advantage of fuzzy numbers in order to summarize information. A fuzzy model is then presented, to be considered as a decision making aid tool. In this way we generalize the classical definition of fuzzy partition due to Ruspini, allowing in addition a first evaluation of the quality of the classification in this way obtained, in terms of three basic indexes (measuring covering, relevance and overlapping of our family of classes).
PB Springer-Verlag
SN 1432-7643
YR 2008
FD 2008
LK https://hdl.handle.net/20.500.14352/50094
UL https://hdl.handle.net/20.500.14352/50094
LA eng
NO Gomez, D., Montero, J.: Fuzzy sets in remote sensing classification. Soft Comput. 12, 243-249 (2007). https://doi.org/10.1007/s00500-007-0201-z
NO Universidad Complutense de Madrid
NO University of California
DS Docta Complutense
RD 9 abr 2025