Land Cover Classification in Makkah Area using Frequency-based Contextual Classifier

Classification of land cover is one of the most important tasks and one of the primary objectives in the analysis of remotely sensed data. Recall that the aim of the classification process is to assign each pixel from the analyzed scene to particular class of interest such as urban area, water, land, etc. The image resulting from labeling of all pixels is referred as thematic map. Such maps are very useful in many remote sensing applications especially those concerned with agriculture monitoring, land change detection and environmental purposes. The objective of this study is to evaluate the types of land cover in Makkah area by using Frequency-based Contextual (FBC) classification technique. Contextual classifier that utilized both spectral and spatial information is able to reduce the speckle error and improve the classification performance significantly. A total of five classes could be classified clearly within the study area and 82.3% accuracy was achieved in the classification process.