## 1 Introduction  
Land cover monitoring of urban areas provides vital information in several fields, such as environmental science, seismic risk assessment, urban management, and regional planning. For instance, in sustainable development, urban growth assessment plays an essential role in maintaining the balance between the city and the hinterland. Urban expansion results in the change of urban land cover and the expansion of a city's border, which is necessary for accommodating a growing population and providing them with public city services [1]. Furthermore, it is important to continuously update land cover maps at macro- and micro-scales, which helps governments to be prepared for emergency monitoring of cities, especially after natural hazards [2; 3; 4; 5; 6; 7; 8; 9; 10; 11].  
Remote sensing technologies are fundamental tools used to obtain information from the ground surface to determine land cover classification. Thus, satellite imagery can be used to analyze urban growth and land cover changes [12]. Moreover, urban residential areas with different properties and densities can be identified [13; 14]. One of the most advanced technologies is synthetic aperture radar (SAR) sensors, which have several advantages over optical sensors that are used to capture land surface imagery. SAR sensors can extract object characteristics from backscattering echo, independent of weather conditions and time [15; 16; 17]. Currently, this technology, with dual- or full-polarization(HH, HV, VV and VH), is used widely to monitor urban areas and map land cover since different polarizations have different sensitivities and scattering coefficients for the same target [3; 18; 19; 20; 21].  
Furthermore, texture features represent a significant source of information regarding the spatial relation of pixel values. Thus, different features, such as built-up urban areas, soil, rock, and vegetation, can be more accurately characterized. Many texture measures have been developed and properly used in satellite image analyses. Thus, it is generally accepted that the use of textural images improves the accuracy of land cover classification [22; 23; 24; 25]. Previous research on texture feature extraction showed that the gray-level co-occurrence matrix (GLCM) is one of the most trustworthy methods for classification [26; 27].  
Tehran, the capital city of Iran, has been undergoing rapid changes in land cover and land use, similar to many other metropolitan areas in developing countries. The population of Tehran increased from 6,758,845 in 1996 to 12,183,391 in 2011 [28], almost doubling in only 15 years. For that reason, Tehran is considered as one of the fastest growing cities in the world. Therefore, urban area monitoring of Tehran seems necessary. However, because of the similarity of spectral signatures between soil and roof materials in the built-up areas of Tehran, the accuracy of land cover classification using optical images is expected to be not so high. On the other hand, a SAR image analysis using backscattering intensity data has the potential to accurately classify urban areas.  
In this study, the capabilities of SAR images to recognize built-up areas from bare land in Tehran city, Iran, are evaluated. For this purpose, ALOS-2 PALSAR-2 (HH, HV) and Sentinel-1 C-SAR (VV, VH) are used for land cover classification. Texture measures are applied to the backscatter values of the L- and C-bands of the mentioned satellite. Then, supervised land cover classification of Tehran is carried out using the backscattering intensity and texture measures selected by a principal components analysis. This study attempts to examine the performance of SAR intensity data for land cover classification in arid and semi-arid regions.