CONCLUSIONS

This work has shown that use of spatial information along with ancillary and derived geographical layers is an effective way to improve classification performance, which was demonstrated through implementation in three different terrains. In a highly urbanised area with less vegetation cover and highly contrasting features, inclusion of temperature, NDVI, EVI, elevation, slope, aspect, PAN and texture significantly increased the overall accuracy by 7.6%. In a forested landscape with moderate elevation, temperature was the only factor that increased the LC classification accuracy by 6.7%. In a rugged terrain with temperate climate, temperature, EVI, elevation, slope, aspect and PAN significantly improved the classification accuracy by 10.84% compared to the classification of only original spectral bands.