This paper presents a methodology for image classification of both visual and spectral data with the use of
hybrid techniques represented by soft computing to classify objects from satellite images. In this article the searching
capability of a Fuzzy c-Means Model (FCM) has been exploited for automatically evolving the number of clusters as
well as proper clustering of data set. This paper concerns with classifying five kinds of objects (Residential area,
Agriculture area, Road, River and Tennis stadium). Accordingly. The database which describes that objects depending
on their attributes were built, then, c-means clustering algorithm to the Fuzzy c-Means Algorithm (FCMA) was used.
After that, two types of features for each cluster were extracted. Then, unsupervised classification algorithm (Kohonen
winner-take-all network) determines the class under which each feature vector belongs to was used. At the last stage,
(IF-Then rule) to form several rules that govern each class attributes were used. The proposed methodology provides
fast and adaptive learning for image classification.