Eigenvalue-Based Urban Area Extraction Using Polarimetric SAR Data

Urban area extraction using polarimetric synthetic aperture radar (PolSAR) data has become a potential mean to urban studies because it holds the promise that the radar returns from specific scattering characteristics may be emphasized. Due to the high variability of urban land-scape and the existing misdetection of buildings as vegetation, urban area extraction is still a challenging problem. In this paper, an eigenvalue-based urban area extraction method is proposed. First, similar to the entropy/anisotropy plane, a two-dimensional RVI/PA plane is put forward to construct the extractor of buildings with small orientation angles. Second, coupled with the parameters, a robust extractor is introduced to elevate the scattering characteristics of buildings with large orientation angles but to suppress those of others. Finally, data-driven thresholds are investigated and ascertained for the extractors, thus urban areas are extracted. In addition, a change detection-based prescreening method is applied to refine the extraction result. The performance of the proposed method is demonstrated and validated with spaceborne and airborne fully PolSAR data over different test sites. The outputs show that the proposed method provides an overall accuracy of over 90%, as well as better visual results of the extracted buildings.