Urban construction area extraction using circular polarimetric correlation coefficient

Abstract

Urban construction area detection is of great significance for tracking, mission planning, training, loss estimation and urban planning. In this paper, we make full use of the polarization characteristics of SAR (synthetic aperture radar) data to detect urban construction area. First, circular polarization correlation coefficient characteristics, entropy characteristics based on the gray level co-occurrence matrix (GLCM), and the dihedral angle scattering characteristics using the Pauli decomposition are extracted to distinguish among urban area, forest area and other manmade targets. And then we adopt the three kinds of characteristic to form feature vector and complete urban area detection based on k-means clustering analysis. The experimental result has proved the efficiency of this method.