Superpixel-Based Unsupervised Classification of PolSAR Imagery Using Wishart Mixture Models and Spectral Clustering

Konferenz: EUSAR 2016 - 11th European Conference on Synthetic Aperture Radar
06.06.2016 - 09.06.2016 in Hamburg, Germany

Tagungsband: EUSAR 2016

Seiten: 4Sprache: EnglischTyp: PDF

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Yang, Xiangli; Yang, Wen; Song, Hui (School of Electronic Information, Wuhan University, China)
Huang, Pingping (Radar Research Institute, College of Information Engineering, Inner Mongolia University of Technology, China)

Unsupervised classification of polarimetric synthetic aperture radar (PolSAR) imagery is an essential step in SAR image interpretation. In this paper, we propose a framework for superpixel-based unsupervised classification of PolSAR imagery. Firstly, the SLIC super-pixel algorithm is adapted for generating compactness local regions. Secondly, Wishart Mixture Models (WMM) are learned to model each local region and two analytic information-theoretic divergences are employed for computing the (dis)similarities of region pairs. Finally, the classification results are obtained by using the spectral clustering approach. The experimental results on different SAR data sets show the effectiveness of our method.