Eigenvalue Statistical Components-Based PU-Learning for PolSAR Built-Up Areas Extraction and Cross-Domain Analysis

Polarimetric synthetic aperture radar (PolSAR) provides important support for the built-up areas (BA) information analysis, due to the ability of weather-independent imaging and sensitivity to targets scattering and geometric characteristics. However, PolSAR BA with large orientation angles is usually misdetected as vegetation, and labeled BA samples with special orientations are difficult to obtain. Furthermore, the labeled BA samples and trained models can hardly work well in the cross-domain PolSAR imagery BA analysis. This article presents a PolSAR BA extraction method based on eigenvalue statistical components (ESC) and PU-Learning (PUL), and it helps to realize cross-domain BA extraction by combining subspace alignment (SA). First, the roll invariance of coherency-matrix eigenvalues and building orientation effects are analyzed. Then, by adopting the eigenvalue-Wishart unsupervised classification, regional statistical information and rotation-invariant property are comprehensively utilized in ESC. Finally, the BA can be extracted by combining a PUL classifier with only positive samples at the same distinguishable orientation. Combined with SA, the novel ESC-PUL-SA domain adaptation facilitates a robust unsupervised cross-domain PolSAR BA analysis, reducing the differences caused by sensors and imaging scenes. The ESC-PUL BA extraction on seven PolSAR imageries showed that the accuracies reach 92%–98% with only a few positive samples (less than 0.65%). The ESC-PUL-SA performance was further validated by 14 unsupervised cross-domain BA analysis units among 10 datasets, including Radarsat-2, Gaofen-3, AirSAR, and UAVSAR images. With randomly selected positive samples from the source domain, the proposed ESC-PUL-SA achieved accuracies of all cross-domain BA extraction range from 89.64% to 95.53%.