Bahram Jomeh Zadeh; Sirus Hashemi; Ali Darvishi Bolourani; Majid Kiavarz
Abstract
Using satellite images with a medium spatial resolution to detect, monitor and predict urban built-up areas, has developed in recent decades. The most important step in predicting of the urban areas growth is extracting the urban features with a high precision but the greatest challenge in this way is ...
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Using satellite images with a medium spatial resolution to detect, monitor and predict urban built-up areas, has developed in recent decades. The most important step in predicting of the urban areas growth is extracting the urban features with a high precision but the greatest challenge in this way is the complexity of urban components and the issue of mixed pixels. The purpose of this research is using sub-pixel analysis to extract the surface features of Rasht city to predict the future growth of the city’s built-up areas changes. To achieve this purpose, we used three Landsat images related to; 1990 (Landsat Sensor TM), 2002 (Landsat Sensor ETM +) and 2015 (sensor OLI / TIRS) years and Normalized Spectral Mixture Analysis (NSMA). In order to classify the images, the fraction layers were used as input layers, andend members were used as training samples and maximum likelihood algorithm was used as classifying algorithm. As a result, the overall accuracy of over99% and the kappa coefficient of over 89% were achieved for the images of three periods of study. In this research, however, in order to predict the urban growth by ANN model, Multilayer Perceptron (MLP) with Back-Propagation learning algorithm (BP) were used. The results of comparison between the model’s output and the classification map of 2015 showed a 92% kappa coefficient, an 89% standard Kappa and a 93% classification Kappa (for classes), respectively. The used model in this research has been successful in predicting the growth of urban boundaries, but less accurate in predicting the individual built-up areas around the urban areas.
