Automatic Extraction of Urban Areas Using Sentinel-1 Synthetic Aperture Radar Images

In this paper, we present a novel method for urban area extraction using Sentinel-1 synthetic aperture radar (SAR) imagery. The method utilizes an unsupervised K-means classifier applied to the total backscatter power from dual-polarimetric channels in Sentinel-1 SAR data. To validate the approach, we tested it on two datasets: one from the Algiers region and another from the San Francisco region. Results demonstrate that the proposed method effectively distinguishes urban areas, showing strong potential for accurate urban mapping. Additionally, it reliably identifies the three principal scattering mechanisms on the Earth’s surface (double-bounce, volume, and surface scattering) offering a comprehensive tool for urban and environmental applications.