ALRANAIE M., SAFIANIAN A., POURMANAFI S., MIRGHAFARI N., TARKESH ESFAHANI M.,, ADVANCES IN SPACE RESEARCH, Vol. 61, No. 6, PP. 1558_1572, 2018.

Abstract: In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in theenvironmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolutionWorldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART,Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods:random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniqueswas used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data.Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed.In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boostingmodel is the best performing method whilst based on independent validation there was no significant difference between the performancesof classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine hadbetter processing speed than other.