Labeled Co-occurrence Matrix for the Detection of Built-up Areas in High-Resolution SAR Images

The characterization of urban environments in synthetic aperture radar (SAR) images is becoming increasingly challenging with the increased spatial ground resolutions. In SAR images having a geometrical resolution of few meters (e. g. 3 m), urban scenes are roughly speaking characterized by three main types of backscattering: low intensity, medium intensity, and high intensity, which correspond to different land-cover types. Based on the observations of the behavior of the backscattering, in this paper we propose the labeled co-occurrence matrix (LCM) technique to detect and extract built-up areas. Two textural features, autocorrelation and entropy, are derived from LCM. The image classification is based on a similarity classifier defined in the general Lukasiewicz structure. Experiments have been carried out on TerraSAR-X images acquired on Nanjing (China) and Barcelona (Spain), respectively. The obtained classification accuracies point out the effectiveness of the proposed technique in identifying and detecting built-up areas compared with the traditional grey level co-occurrence matrix (GLCM) texture features.