1. A method to perform rough-wavelet based analysis of spatio-temporal patterns, the method comprising: generating a wavelet granulated space of features associated with a multispectral image, wherein the wavelet granulated space constitutes 4 n granules in an n-dimension feature space for a one-level discrete wavelet transform (DWT) decomposition and 7 n granules in the n-dimension feature space for a two-level DWT decomposition; selecting features based on a rough set evaluation; removing redundant features; in response to removal of at least one of the redundant features, determining a measure of significance of the features by an evaluation of a change in data dependency of the features, wherein a greater change in data dependency indicates a greater measure of the significance; classifying the spatio-temporal patterns based on the selected features; and locating and selecting a subset of granulated features based on the significance.