A Semi-automated Approach for the Production of Land-cover Change Maps Using Fuzzy Sets and Remotely Sensed Data

This paper presents the framework for the implementation of a non-heuristic technique for thresholding of change images derived from multi-temporal analysis of remotely sensed data using pre-classification techniques. The approach is based on fuzzy sets and fuzzy logic, and it assumes that accurate separation of change/no-change areas can be achieved if the membership function of the fuzzy model is adapted to the shape of the histogram of the change image. The output from the model is a ‘possibility of changes’ image, as opposed to the traditional binary change/no-change image. The accuracy in the separation of change/no-change areas is assessed using the error matrix and its associated user’s, producer’s and overall accuracy measures. The overall and per class kappa coefficient are used as additional measures of accuracy. The study also compares the performance of the ‘fuzzy thresholding’ against the ‘symmetric thresholding’, and determines a fuzzy linguistic value (and its associated fuzzy interval) that better reflect the separation between areas of change/no-change.