Application of remote sensing data in landuse classification often comes cross some difficulties and problems that originate from various types of uncertainty associated with image information extraction and ambiguity of the linguistic rules involved in the context information concerning dependency between features and landuse.Fuzzy classification system,as one of the most powerful soft classifiers,is capable of incorporating inaccurate sensor measurements,vague class descriptions and imprecise modeling in the analysis process,and outputting classification results that better demonstrate the limitation of human knowledge and the real world.Therefore,fuzzy classification is considered as a better method in landuse mapping based on remote sensing data.In this paper,a case study of the periurban Nanjing was carried out to extract landuse information by means of the supervised fuzzy classification,based on object-oriented segmentation and the resultant so-called image object information of not only spectral values,but also feature space using shape and topological features.Results indicte that fuzzy classification of landuse based on remote sensing data could achieve a more reasonable and meaningful result,in comparison with conventional rigid methods.