Polarimetric synthetic aperture radar (PolSAR) building extraction plays an important role in urban planning, disaster management, etc. In this paper, a building extraction method using refined model-based decomposition and robust scattering feature is proposed. On the one hand, the newly proposed refined five-component decomposition and its derived scattering powers are applied to detect the buildings. On the other hand, by combining the matrix elements and co-polarization correlation coefficient, a robust feature is proposed to discriminate buildings and non-buildings. Both these two preliminary extraction results are obtained through thresholding segmentation. Finally, they are fused via the HX Markov random fields so as to further improve the extraction accuracy. The performance of the proposed method is demonstrated and evaluated with Gaofen-3 and uninhabited aerial vehicle SAR full PolSAR data over different test sites. Outputs show that the proposed method outperforms other state-of-the-art methods and provides an overall accuracy of over 90%.

Due to incomprehensive and inaccurate scattering modeling, the state-of-the-art polarimetric synthetic aperture radar (PolSAR) model-based target decompositions are incapable of effectively depicting the scattering mechanism of obliquely oriented urban areas. In this paper, a seven-component model-based decomposition scheme is proposed by constructing several sophisticated scattering models. First, an eigenvalue-based obliquely-oriented dihedral scattering model is presented to reasonably distribute the co-polarization and cross-polarization scattering powers in obliquely oriented urban areas, thus accurately characterizing the urban scattering. Second, the ±45° oriented dipole and ±45° quarter-wave reflector scattering models are incorporated for the purpose of accounting for the real and imaginary components of the $T_{13}$ element in the coherency matrix so as to fully utilize polarimetric information. Finally, according to their mathematical forms, several strategies for model parameter solutions are designed, and the seven-component decomposition is fulfilled. Experimental results conducted on different PolSAR data demonstrate that the proposed method considerably improves the PolSAR scattering interpretation in a more physical manner compared to other existing model-based decomposition, which can be applied for urban area detection, classification, and other urban planning applications.