Monitoring urban land through satellite images has rapidly developed with the advent of modern technologies, and the increasing number of satellites plays a contributing role. While optical images have a high capability in urban monitoring, they still have some limitations, including their dependence on climatic conditions and spectral information, which lead to difficulty in making a distinction between bare land, buildings and other features. The impossibility of optical imagery at night is another issue that can make the land cover classification difficult. Synthetic aperture radar (SAR) allows imaging in all climatic conditions and at nighttime, with an ability to detect phenomena based on their geometry, roughness, and location, making the land cover classification much easier. In the present study, radar Sentinel-1 images with polarization VV and VH were used for the land classification in Tabriz. Sentinel-2 images for the same time were applied as a reference for the calibration and accuracy assessment. Maximum likelihood (ML) and support vector machine (SVM) algorithms were also employed for supervised classification. In both algorithms, the classification was performed in windows with different sizes once by the SAR backscattering coefficient (σ⁰) and then by combining the backscattering coefficients with the statistical data obtained from the texture. The results showed that the use of radar images only with backscattering intensity resulted in poor performance while using the gray-level co-occurrence matrix (GLCM) of texture features increased the accuracy. The transmitted frequencies of radar images have different redistributions to different phenomena. The numerical results obtained from the radar image classification show that using only the radar image redistribution led to low accuracies at both VV and VH polarization, but the use of the textural analysis significantly increased the accuracy of the classifications. The statistical results obtained from the ML and SVM classifications for radar images at VV and VH polarization indicated that the latter performed better than the former. When texture analysis was not used in the classes, the classification accuracy was low with kappa values of 0.37 and 0.42 for VV and VH polarization, respectively. The use of texture analysis and obtaining the optimum window size is increase the classification accuracy with a better performance for VH polarization. The SVM classification method with a kappa coefficient of 0.72% showed better performance than the ML one with a kappa coefficient of 0.61%. Conclusively, in the absence of Sentinel-2 datasets, Sentinel-1 images are good alternatives if the preserved texture information is available for the land cover classification. Results of this research are of great importance for developing the remote sensing methods and their techniques can be considered as progressive research in the domain of remote sensing sciences.

随着现代技术的出现，通过卫星图像监测城市土地已经迅速发展，越来越多的卫星起着重要的作用。尽管光学图像在城市监测中具有很高的能力，但它们仍然存在一些局限性，包括它们对气候条件和光谱信息的依赖，这导致难以在裸露的土地，建筑物和其他特征之间进行区分。夜间无法进行光学成像是另一个可能使土地覆被分类变得困难的问题。合成孔径雷达（SAR）允许在所有气候条件下和夜间进行成像，并能够根据其几何形状，粗糙度和位置来检测现象，从而使土地覆盖分类变得更加容易。在目前的研究中，在大不里士，使用极化VV和VH极化的Sentinel-1雷达图像进行土地分类。同时使用Sentinel-2图像作为校准和准确性评估的参考。最大似然（ML）和支持向量机（SVM）算法也用于监督分类。在这两种算法中，通过SAR反向散射系数（σ ⁰），然后将反向散射系数与从纹理获得的统计数据结合起来。结果表明，仅使用具有反向散射强度的雷达图像会导致性能不佳，而使用纹理特征的灰度共生矩阵（GLCM）则会提高精度。雷达图像的发射频率对不同现象具有不同的重新分布。从雷达图像分类获得的数值结果表明，仅使用雷达图像重新分布会在VV和VH极化下导致较低的准确性，但是使用纹理分析显着提高了分类的准确性。从VV和VH极化的雷达图像的ML和SVM分类获得的统计结果表明，后者的性能要优于前者。当在类别中不使用纹理分析时，分类准确度较低，VV和VH极化的kappa值分别为0.37和0.42。使用纹理分析并获得最佳窗口大小可提高分类精度，并具有更好的VH极化性能。Kappa系数为0.72％的SVM分类方法的性能优于Kappa系数为0.61％的ML方法。结论是，在没有Sentinel-2数据集的情况下，如果保留的纹理信息可用于土地覆被分类，则Sentinel-1图像是不错的选择。