Metric learning based collapsed building extraction from post-earthquake PolSAR imagery

Abstract

In this paper we proposed a metric learning-based method to extract collapsed buildings from post-earthquake PolSAR imagery. In this method, eight building and orientation related features, including entropy H, the average scattering angle α, anisotropy A, the circular polarization correlation coefficient ρ and the four scattering powers of Yamaguchi 4 component decomposition with a rotation of the coherency matrix, are considered and analyzed. Then a transformation matrix is learned from collapsed and intact building samples via an improved informational-theoretic metric learning(ITML). With such a transformation matrix, the features are projected into a low-dimension space to mitigate the impact of topography and building's aspect angle. Finally a k − NN classifier is utilized to distinguish collapsed and intact buildings. The proposed method is tested on one RadarSAT-2 PolSAR image acquired after 2010 Yushu Earthquake in the Qinghai Province of China. Results are validated by the manually interpretation map of a very high resolution (VHR) optical image. It shows that, the method is efficient to extract collapsed building areas using limited samples and only one post-earthquake PolSAR image.