classification algorithm

Land use identification and determination of their spreading level in the region is one of important factors in the natural resources and environmental studies. Preparing land use map from satellite data is one of the fastest and most cost-effective methods. This study’s aim is to determine the best algorithm for TM satellite images classification between 6 supervised classification methods including maximum likelihood, mahalanovis distance, minimum distance, parallel pipe, support vector machines and binary codes to extract land use map of Zenouz Chai watershed.For grassland, wasteland and abandoned and agricultural land use, 30 training samples and for badland land use 45 training samples were prepared separately using ground control points. Results were assessed for satellite image classified using by overall accuracy index, kappa coefficient, producer accuracy and user accuracy. Investigation of TM image classification accuracies showed that the maximum likelihood algorithm with overall accuracy coefficient (84/73 %) and kappa coefficient 65/0, have the higher efficiency for image classification into four land use classes. The results showed that maximum likelihood method has more capabilities than other methods for land use map preparing. Therefore, the results of this study can be used to provide land use maps with higher accuracy using maximum likelihood method to assessing the environment and natural resources works in the areas with the same situations.