RS based LC mapping and monitoring of large areas has created new challenge with the varied spatial scale and data volume, requiring automated classification algorithms that minimise human interventions. This work has shown that use of spatial information along with ancillary and derived geographical layers is an effective way to improve LC classification performance which was demonstrated in an urban terrain.

In a highly urbanised area with less vegetation cover and highly contrasting features, texture played a major role in discriminating individual classes which were rather difficult todistinguish using only original high spatial resolution IKONOS MS bands. DEM plays a role when the terrain is undulating, however, due to limited vegetation cover, vegetation index was not useful in classification. For the same urban area, inclusion of temperature, NDVI, EVI, elevation, slope, aspect, PAN and texture significantly increased the overall accuracy by 7.6% while discriminating different classes properly with Landsat ETM+ data.