模糊聚类是非监督分类中的一类重要方法。传统的模糊聚类方法应用于遥感影像的非监督分类时，均未考虑到邻域像元间的统计依赖关系即上下文信息。针对这一缺陷，在Markov随机场模型框架下，引入了空间隶属度概念，提出了一种顾及上下文信息的模糊聚类算法，有效地提高了聚类精度和抗噪声能力。针对需要预先指定聚类个数的问题，采用了一种兼顾类别内部紧密程度和类别之间分离程度的评价指标，用以检验聚类结果的有效性。从而找出最优的聚类个数，在一定程度上提高了聚类结果的客观性。最后通过实验验证了本文算法的有效性。

Fuzzy clustering is an important method in unsupervised classification. In application of traditional fuzzy clustering algorithm to unsupervised classification of remote sensing imagery, pixels are assumed to be independent of each other and their fuzzy memberships are determined individually, so that context information, i.e. statistical dependencies among neighboring pixels, are not taken into account. Aiming at this problem, an improved fuzzy clustering algorithm considering context information is put forward by incorporating the concept of spatial fuzzy membership under MRF framework. In this way, accuracy and reliability of clustering can be improved upon traditional ones.\r\n To evaluate the quality of clustering results, a validation index considering both intra-cluster compactness and inter-cluster separation is introduced, further more it is employed to find out naturally optimal cluster numbers and promote objectivity of clustering results. Finally an experiment on real remote sensing imagery is carried out to demonstrate the effectiveness of our proposed scheme.