Image pre-processing and classification
Satellite image pre-processing is an improvement of satellite data that suppresses unwanted distortions and main objective to generating a more direct linkage between the obtained data and real world phenomena (Coppin et al., 2004; Butt, et al., 2015). Satellite data were preprocessed in ERDAS imagine 14 software for layer stacking, mosaicking, and subsetting of the image on the basis of Area of Interest (AOI). All image data were assessed by allocating per-pixel signatures and distinguishing the Islamabad capital into five classes with reference to specific Digitial Number (DN) value. The identified land cover classes were built-up area, agriculture, water bodies, forest and barren land (Table 2). For the individual pre-determined land cover type, training sites were identified by demarcating polygons around representative sites. In this study, for supervise classification thirty spectral signatures for each class were taken from the each satellite imagery by using the pixels enclosed by these polygons. According to Gao and Liu (2010) an adequate spectral signature is the one ensuring that there is minimal confusion among the land covers to be mapped. The maximum likelihood algorithm was applied for supervised classification of the images. Supervise classification is mainly controlled by the analyst as the analyst choose of the pixels which demonstrate the desired classes. For medium spatial resolution data, such as Landsat mixed pixels are a common problem, especially for the urban surfaces are a mixture of features such as build-up land, grass, trees, roads and water. For the improvement of classification accuracy and to produce quality of land LULC maps, visual interpretation is very important. Therefore, with help of field observations, reference data as well as local knowledge can improve the results acquired using the supervised algorithm (Jensen and Im, 2007; Butt et al., 2015).