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Classification procedure aims at finding regions of the classes in the feature space. There are several algorithms proposed with supervised and unsupervised strategies for classification in literature. This paper goes on to propose a supervised method of classification using information slicing. Information lies in the feature space of the data to be classified. Training stage consists of slicing of the information by continuous partitioning of the feature space to find pure regions for classes from training data set. Classification then becomes a find-and-assign problem in the feature space for the input data. It has been shown here that this method of classification works well on data which have highly overlapping regions amongst the classes. This classification method is further applied for object classification in satellite imagery, where objects are homogeneous regions in images of considerably high resolution. Identification of homogeneous regions is done by a graph based segmentation algorithm which searches for segments having high intra region pixel similarity and high inter-region pixel dissimilarity. Feature vectors for each of these objects are calculated and then these feature vectors are given as an input to the information slicing classifier. Ground truth however is needed for the training stage of the classifier which has been generated here by manual intervention. Experiments have been done on four classes and results show that the method of information slicing works well on selective classes.