An experiment for the interpretation of multitemporal remotely sensed images based on a fuzzy logic approach

Abstract

A rule-based classification system for images remotely sensed by satellites is presented. The spectral characterization of the land-cover classes studied has been carried out by exploiting rules which are based on the knowledge of an expert agronomist, and applying them to a multitemporal Thematic Mapper image dataset. The system knowledge base consists of a set of rules describing each class on each date. In the construction of these rules the system adopts linguistic descriptors such as low, high, very low, and any logical combination of these predicates as the user interface. These linguistic predicates are treated as membership functions for ‘fuzzy sets’ defined on the image spectral values. The study has been carried out on a hilly area in Southern Italy which features a great variety both in terms of land use classes and in terms of agricultural practices. The following I/II level classes have been investigated: cropland, forestry, vineyards, olive-groves, pasture, bare soils and urban area. The results of the rule-based approach are compared with a maximum likelihood supervised classification approach.