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

Abstract: 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 class-dependent (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 classes. Neighborhood rough sets (NRS) are employed in the selection of a subset of granulated features that further explore the local/contextual information from neighbor granules. The model thus explores mutually the advantages of shift-invariant wavelet granulation and NRS. The superiority of the proposed model to other similar methods is established both visually and quantitatively for land cover classification of multispectral remote sensing images. With experimental results, it is found that the proposed model is superior with biorthogonal3.3 wavelet, and when integrated with NRS, it performs the best.