RT Book, Section
T1 Accuracy measures for fuzzy classification in remote sensing
A1 Gómez González, Daniel
A1 Montero De Juan, Francisco Javier
A1 Biging, Greg
AB Over the last decades, many fuzzy classification algorithms have been proposed for image classification,and in particular to classify those images obtained by remote sensing. But relatively little effort has been done to evaluate goodness or effectiveness of such algorithms. Such a problem is most of the times solved by means of a subjective evaluation, meanwhile in the crisp case quality evaluation can be based upon an error matrix, in which the reference data set (the expert classi-fication) and crisp classifiers data set are been compared using specific accuracy measures. In this paper,some of these measures are translated into the fuzzy case, so that more general accuracy measures between fuzzy classifiers and the reference data set can be considered.
PB EDK
SN 2-84254-112-X
YR 2006
FD 2006
LK https://hdl.handle.net/20.500.14352/53408
UL https://hdl.handle.net/20.500.14352/53408
LA eng
NO International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (11ª. 2006. París)
NO Gobierno de España
DS Docta Complutense
RD 4 abr 2025