Improving fuzzy classification by means of a segmentation algorithm
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2008
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Springer
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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)
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.