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Improving fuzzy classification by means of a segmentation algorithm

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Amo, Ana del and Gomez, Daniel and Montero, Javier (2008) Improving fuzzy classification by means of a segmentation algorithm. In Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, II (220). Springer, Berlin, pp. 453-471. ISBN 978-3-540-73722-3

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Official URL: http://link.springer.com/chapter/10.1007%2F978-3-540-73723-0_23


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Abstract

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.


Item Type:Book Section
Subjects:Sciences > Mathematics > Logic, Symbolic and mathematical
ID Code:29032
Deposited On:05 Mar 2015 11:08
Last Modified:20 Apr 2016 13:33

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