SCFN: A Deep Network for Functional Urban Impervious Surface Mapping Using <italic>C</italic>-Band and <italic>L</italic>-Band Polarimetric SAR Data

Accurate and timely monitoring of functional urban impervious surfaces (FUISs), such as ports, roads, and buildings, is essential yet challenging for complex coastal cities due to their cloudy weather and diverse land surfaces. Synthetic aperture radar (SAR) provides unique all-weather observation capabilities for prompt and regular urban mapping. However, SAR scattering information is limited to distinguish impervious surfaces with similar scattering responses but different functions. This study develops a scattering&#x2013;compactness fusion network (SCFN), which integrates SAR polarimetric scattering and object compactness characteristics for enhanced FUIS recognition. Central to our approach is the scattering object compactness index, which is specifically designed to capture the distinct spatial patterns and compactness of scattering objects and complement their intrinsic scattering signatures. The dual-branch SCFN concurrently extracts and fuses object-scale scattering and compactness features using tailored network architectures. Experiments on <italic>L</italic>-band and <italic>C</italic>-band fully polarimetric ALOS-2 and GF-3 data in Hong Kong, as well as <italic>L</italic>-band dual-polarized ALOS-2 data, are undertaken to verify SCFN&#x0027;s effectiveness, achieving up to 8&#x0025; improvement in the overall FUIS classification accuracy over baselines. The transferability of SCFN is further validated using fully polarimetric ALOS-2 data in Shenzhen, where consistent performance improvements are observed. The successful application of SCFN in both coastal cities highlights the potential of joint scattering&#x2013;compactness modeling for advanced SAR-based urban mapping and its robustness across different urban landscapes.