Comparing the performance of fuzzy and crisp classifiers on remotely sensed images: a case of snow classification

This study deals with the evaluation of accuracy benefits offered by a fuzzy classifier as compared to hard classifiers using satellite imagery for thematic mapping applications. When a crisp classifier approach is adopted to classify moderate resolution data, the presence of mixed coverage pixels implies that the final product will have errors, either of omission or commission, which are not avoidable and are solely due to the spatial resolution of the data. Theoretically, a soft classifier is not affected by such errors, and in principle can produce a classification that is more accurate than any hard classifier. In this study we use the Pareto boundary of optimal solutions as a quantitative method to compare the performance of a fuzzy statistical classifier to the one of two hard classifiers, and to determine the highest accuracy which could be achieved by hard classifiers. As an application, the method is applied to a case of snow mapping from Moderate-Resolution Imaging Spectroradiometer (MODIS) data on two alpine sites, validated with contemporaneous fine-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. The results for this case study showed that the soft classifier not only outperformed the two crisp classifiers, but also yielded higher accuracy than the maximum theoretical accuracy of any crisp classifier on the study areas. While providing a general assessment framework for the performance of soft classifiers, the results obtained by this inter-comparison exercise showed that soft classifiers can be an effective solution to overcome errors which are intrinsic in the classification of coarse and moderate resolution data.