Urban land cover classification from high resolution multi-spectral IKONOS imagery

We analyzed the effectiveness of generating urban land cover maps from IKONOS imagery. 1-m PAN and 4-m MS IKONOS images were combined to produce two pan-sharpened MS images (PS-MS) with 1-m resolution. The fusion was done with the original 11-bit data and also by scaling the original data to only 8-bits. A parallelepiped supervised classification algorithm was used to process the two PS-MS images as well as the original 4-m MS image. Seven urban land cover classes were used in this study: woods, grass, water, bare soil, commercial building, impervious, and shadow. The classification accuracy was assessed using 256 pixels that were randomly distributed throughout the test site and were independent of the training sites used by the supervised classification algorithm. The results show that classification accuracies on the order of 75-80% are obtained. The best results are obtained using the 4-band 11-bit 1-m PS-MS image, and this yielded an overall accuracy of 83%.