Using Post-Classification Enhancement in Improving the Classification of Land Use/Cover of Arid Region (A Case Study in Pishkouh Watershed, Center of Iran)

Classifying remote sensing imageries to obtain reliable and accurate land use/cover (luc) information still remains a challenge that depends on many factors such as complexity of landscape especially in arid region. the aim of this paper is to extract reliable luc information from land sat imageries of the pishkouh watershed of central arid region, iran. the classical maximum likelihood classifier (mlc) was first applied to classify land sat image of 15 july 2007. the major luc identified were shrubland (rangeland), agricultural land, orchard, river, settlement. applying post-classification correction (pcc) using ancillary data and knowledge-based logic rules the overall classification accuracy was improved from about 72% to 91% for luc map. the improved overall kappa statistics due to pcc were 0.88. the pcc maps, assessed by accuracy matrix, were found to have much higher accuracy in comparison to their counterpart mlc maps. the overall improvement in classification accuracy of the luc maps is significant in terms of their potential use for land change modeling of the region.