Synthetic aperture radar (SAR) systems enable continuous monitoring of the Earth’s surface. This approach supports various applications like land cover classification, change detection, vegetation monitoring, urban expansion, and natural hazard assessment. SAR data in dual-polarimetric mode provide more detailed backscatter information, which enhances the ability to distinguish between various surface types (e.g., vegetation, water, urban areas). This work focuses on the dual-polarization (VV: vertical transmit/vertical receive, VH: vertical transmit/horizontal receive) and preprocessing analysis of SAR data. Generation of the 2 × 2 covariance matrix [C2] is essential for further analysis. The ESA (European Space Agency) SNAP (Sentinel Application Platform) S1 toolbox can be used to preprocess data in order to produce a [C2] matrix. The covariance matrix is frequently the starting point for polarimetric analysis within the corresponding application fields. Sentinel-1A Single Look Complex (SLC) data are preprocessed, and then classification is done using random forest (RF) and K-nearest neighbors (KNN) algorithms, to distinguish between water, urban, and forest regions. The RF classifier achieved a total accuracy of 94%, while the KNN classifier produced an accuracy of 91.1%. In comparison with the K-nearest neighbors (KNN) classifier, the random forest (RF) classifier obtained higher accuracy and precision results.