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Proceedings Paper

Labeled co-occurrence matrix for the detection of built-up areas in high-resolution SAR images
Author(s): Na Li; Lorenzo Bruzzone; Zengping Chen; Fang Liu
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Paper 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.

Paper Details

Date Published: 17 October 2013
PDF: 12 pages
Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88921A (17 October 2013); doi: 10.1117/12.2029872
Show Author Affiliations
Na Li, National Univ. of Defense Technology (China)
Lorenzo Bruzzone, Univ. degli Studi di Trento (Italy)
Zengping Chen, National Univ. of Defense Technology (China)
Fang Liu, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 8892:
Image and Signal Processing for Remote Sensing XIX
Lorenzo Bruzzone, Editor(s)

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