Rough-wavelet granular space and classification of multispectral remote sensing image

A new rough-wavelet granular space based model for land cover classification of multispectral remote sensing image, is described in the present article. In this model, we propose the formulation of classdependent (CD) granules in wavelet domain using shift-invariant wavelet transform (WT). Shift-invariant WT is carried out with properly selected wavelet base and decomposition level(s). The transform is used to characterize the feature-wise belonging of granules to different classes, thereby producing wavelet granulation of the feature space. The wavelet granules thus generated possess better class discriminatory information. The granulated feature space not only analyzes the contextual information in time or frequency domain individually, but also looks into the combined time–frequency domain. These characteristics of the generated CD wavelet granules are very useful in the pattern classification with overlapping