This talk has two parts explaining the significance of Rough sets in granular computing in terms of rough set rules and in uncertainty handling in terms of lower and upper approximations. The first part describes how the concept of rough-fuzzy granulation can be used for the problem of case generation, with varying reduced number of features, in a case based reasoning framework, and their application to multi-spectral image segmentation.