Fuzzy segmentation for object-based image classification

This Letter proposes an object-based image classification procedure which is based on fuzzy image-regions instead of crisp image-objects. The approach has three stages: (a) fuzzification in which fuzzy image-regions are developed, resulting in a set of images whose digital values express the degree of membership of each pixel to target land-cover classes; (b) feature analysis in which contextual properties of fuzzy image-regions are quantified; and (c) defuzzification in which fuzzy image-regions are allocated to target land-cover classes. The proposed procedure is implemented using automated statistical techniques that require very little user interaction. The results indicate that fuzzy segmentation-based methods produce acceptable thematic accuracy and could represent a viable alternative to current crisp image segmentation approaches.