Detecting Impervious Urban Surfaces using the Textural Properties of Radar Imagery

Urban population growth and the continuous physical development of cities change the natural coverage of the earth and transform it into artificial cover and impervious surfaces. The dramatic increase of these surfaces yields negative consequences in many areas such as increasing surface runoff and flood risk, decreasing groundwater recharge, or intensifying the urban heat island effect. For these reasons, accurate estimation and monitoring of the trend of changes in these ranges is necessary. In this regard, remote sensing data are a cost-effective solution for the preparation and monitoring of impervious surfaces. The purpose of this study was to identify impervious urban surfaces using radar images. In the present study, the textural properties of the Gray Level Co-occurrence Matrix (GLCM) were evaluated using maximum likelihood classification methods, artificial neural network, and support vector machine on Sentinel-1 radar image to determine impervious surfaces of Bandar Abbas city. The overall accuracy of 97.00%, 98.14%, 98.40% and Kappa coefficient of 0.95, 0.97, and 0.97, respectively, for maximum likelihood classification, artificial neural network, and support vector machine, indicated the appropriateness of the utilized methods for detecting impervious urban surfaces. For extracting urban surface information, spectral feature classification algorithms are mostly used. This causes a large amount of useful spatial information such as texture to be ignored in the classification images. Given that SAR images are sensitive to the geometrical properties of urban surfaces, impervious urban surfaces can be accurately detected by using textural properties of radar imagery, which has not been addressed so far.