Here, we discuss the improvements in urban classification that were made using the spatial-spectral-angular information from a WorldView-2 (WV-2) multiangle image sequence. In this study, we evaluate the use of multiangle high resolution WV-2 panchromatic (PAN) and multispectral image (MSI) data for extracting urban geospatial information. Current multiangular WV-2 data were classified into misclassification-prone surfaces, such as vegetation, water bodies, and man-made features, using a cluster of normalized difference spectral index ratios (SIR). A novel multifold methodology protocol was designed to estimate the consequences of multiangularity and germane PAN-sharpening algorithms on the spectral characteristics (distortions) of satellite data and on the resulting land use/land cover (LU/LC) mapping using an array of SIRs. Eight existing PAN-sharpening algorithms were used for data fusion, followed by estimation of multiple SIRs to mitigate spectral distortions arising from the multiangularity of the data. This research highlights the benefits of using traditional PAN-sharpening techniques with a specific set of SIRs on land cover mapping based on five available tiles of satellite data. The research provides a method to overcome the atmospherically triggered spectral distortions of multiangular acquisitions, which will facilitate better mapping and understanding of the earth’s surface.