Fuzzy logic & image classification

Fuzzy logic is relatively young theory. Major advantage of this theory is that it allows the natural description, in linguistic terms, of problems that should be solved rather than in terms of relationships between precise numerical values. This advantage, dealing with the complicated systems in simple way, is the main reason why fuzzy logic theory is widely applied in technique. It is also possible to classify the remotely sensed image (as well as any other digital imagery), in such a way that certain land cover classes are clearly represented in the resulting image. If that’s so, can we use fuzzy logic technique to diminish the influence of person dealing with supervised classification? Can we eliminate the prejudice? These questions were the light motive for this paper. In this paper, a priori knowledge about spectral information for certain land cover classes is used in order to classify SPOT image in fuzzy logic classification procedure. Basic idea was to perform the classification procedure first in the supervised and then in fuzzy logic
manner. The later was done with ©Matlab’s Fuzzy Logic Toolbox. Some information, needed for membership function definition, was taken from supervised maximum likelihood classification. Also, the idea for result comparison came from ©PCI’s ImageWorks used for supervised procedure. Results of two procedures, both based on pixel-by-pixel technique, were compared and certain encouraging conclusion remarks come out
KEY WORDS: fuzzy logic, classification, if-then rules, digital, imagery, remote sensing, land cover