Labeled co-occurrence matrix for the detection of built-up areas in high-resolution SAR images

Na Li; Lorenzo Bruzzone; Zengping Chen; Fang Liu

doi:10.1117/12.2029872

17 October 2013 Labeled co-occurrence matrix for the detection of built-up areas in high-resolution SAR images

Na Li, Lorenzo Bruzzone, Zengping Chen, Fang Liu

Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX; 88921A (2013) https://doi.org/10.1117/12.2029872
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

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.

Citation Download Citation

Na Li, Lorenzo Bruzzone, Zengping Chen, and Fang Liu "Labeled co-occurrence matrix for the detection of built-up areas in high-resolution SAR images", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88921A (17 October 2013); https://doi.org/10.1117/12.2029872

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