The use of fuzzy set theory in remote sensing pattern recognition

Abstract

Satellite images increasingly become a major data source for monitoring changes in the natural environment. A main task in the analysis of satellite images is concerned with the modelling of land use classes by reducing uncertainty during a classification process. In the approach presented in this paper uncertainty is perceived to be due to the vagueness of geographical categories caused by either the complexity of the category (like 'urban area') or by the use of the category in several application contexts. Two circumstances of use of an extended set theoretical concept (fuzzy sets) are discussed. First, two algorithms from the ISODATA class are used to determine the grades of membership to three a priori defined classes (woodland, suburban area, urban area) of a LANDSAT TM satellite image of Vienna, Austria. The results are visualized by a RGB image of the grades of membership to each category. Second, a measure of fuzziness is employed on the results. The measure relies on the concept of distance between a seUcategory and its complement. The so determined vagueness provide more information on the uncertainty of the different categories and may improve further information processing tasks.

Development of hierarchical terron workflow based on gridded data – A case study in Denmark

“…Combining soil and landscape properties would result in transition zones between the unit boundaries, not sharp boundaries produced from hard clustering methods (de Bruin and Stein, 1998). Fuzzy sets have been incorporated in other classification techniques to deal with boundary transitions and to determine the degree a location is within all classes (remote sensing pattern recognition: Fischer and Benedikt, 1997; agroecozones: Liu and Samal, 2002). Fuzzy c-means assesses the vagueness of terron development through the membership map.…”

“…Fuzzy c-means assesses the vagueness of terron development through the membership map. Vagueness is linked to the challenges of delimiting the terron classes (Fischer and Benedikt, 1997). Thus, the membership map is crucial to determine how close each location is to being assigned the optimal terron.…”