RT Book, Section
T1 Improving fuzzy classification by means of a segmentation algorithm
A1 Del Amo, Ana
A1 Gómez González, Daniel
A1 Montero De Juan, Francisco Javier
A2 Bustince, Humberto
A2 Herrera, Francisco
A2 Montero De Juan, Francisco Javier
AB In this chapter we consider remotely sensed images, where land surface should be classified depending on their uses. On one hand, we discuss the advantages of the fuzzy classification model proposed by Amo et al. (European Journal of Operational Research, 2004) versus standard approaches. On the other hand, we introduce a coloring algorithm by to Gòmez et al. (Omega, to appear) in order to produce a supervised algorithm that takes into account a previous segmentation of the image that pursues the identification of possible homogeneous regions. This algorithm is applied to a real image, showing its high improvement in accuracy, which is then measured.
PB Springer
SN 978-3-540-73722-3
YR 2008
FD 2008
LK https://hdl.handle.net/20.500.14352/53400
UL https://hdl.handle.net/20.500.14352/53400
LA eng
NO Amo, A.D., Gómez, D., Montero, J., Biging, G.S.: Improving Fuzzy Classification by Means of a Segmentation Algorithm. En: Bustince, H., Herrera, F., y Montero, J. (eds.) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. pp. 453-471. Springer Berlin Heidelberg, Berlin, Heidelberg (2008)
NO Gobierno de España
DS Docta Complutense
RD 22 abr 2025