Man-made target extraction from space-borne polarimetric synthetic aperture radar（SAR） imagery has been extensively investigated. However, challenging issues caused by polarization orientation angle（POA） shifts and various complex backscattering signatures still remain. Utilizing quad-polarimetric SAR（Quad-Pol SAR） imagery for classification and target detection in urban areas has three major issues:（1） POA shifts;（2） Complex backscattering diversity;（3） Close similarity between targets of weak backscattering signals.This study proposes a novel approach to extract urban synthetic targets from Quad-Pol SAR imagery with roll-invariant parameters to overcome the three aforementioned issues.This study used the dataset of Quad-Pol SAR imagery acquired by Radarsat-2 to extract the synthetic targets in the urban area. Landsat 8 imagery was used as a reference for comparison.The case study area is located in Nanjing City, Jiangsu Province, People’s Republic of China.In the first step of the proposed method, the target scattering vector model, which supplied more parameters than the previously proposed models,was applied to generate the roll-invariant parameters. Subsequently, the relief algorithm suitable for binary classification was introduced to optimize polarization parameters. Considering feature selection and scattering mechanism analysis, two parameters, namely,scattering-type magnitude（parameter αs） and target helicity（parameter τm）, are adopted. Finally, support vector data description was employed to identify synthetic targets because of its efficiency and robustness. In addition to αs and τm, morphological profiles were employed to improve the extracted result.Result shows that the roll-invariant parameters αs1（the domain scattering-type magnitude） and τm2（the secondary scattering helicity of target） are effective for extracting the urban built-up area. Cropland, vegetation, and bare soil can be easily distinguished from the synthetic targets by integrating the parameters τm2 and αs1.The biophysical composition index（BCI） and normalized difference built-up index（NDBI）are derived from optical images. However, these indices confuse bare soil and synthetic targets. The proposed method can well recognize more synthetic targets,such as squares, streets, and other weak backscattering targets than typical decomposition like the Yamaguchi four-component decomposition. The parameters of H/A/alpha decomposition have the roll-invariance property. However,the entropy of urban areas and vegetation are highly similar.Compared with the traditional method of POA compensation applied in the Yamaguchi four-component decomposition, the roll-invariant parameters are more effective in solving the problem of the POA shift.The introduced decomposition model supplies more parameters than H/A/alpha decomposition, which leads to higher accuracy in extraction. The selected parameters αs1（scattering angle characteristics） and τm2（symmetry polarization characteristics） of synthetic targets homogeneously distributed in space, which prevents the built-up area from being segmented into several classes and improves the efficiency of the extraction algorithm. Furthermore, the extracted result shows that the proposed method has significant advantages in distinguishing bare soil and urban targets compared with the results of optical indices, such as BCI and NDBI. The analysis of the scattering mechanism and experimental results reveal the effectiveness of the roll-invariant parameters.

旋转不变极化参数;全极化SAR;人工地物检测;支持向量数据描述;特征选择