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Dabboor, M., Collins, M.J., Karathanassi, V., et al. (2013) An Unsupervised Classification Approach for Polarimetric SAR Data Based on the Chernoff Distance for Complex Wishart Distribution. IEEE Transactions on Geoscience and Remote Sensing, 51, 4200-4213.
https://doi.org/10.1109/TGRS.2012.2227755

has been cited by the following article:

• TITLE: Spectral Clustering with Eigenvalue Similarity Metric Method for POL-SAR Image Segmentation of Land Cover

  AUTHORS: Shuiping Gou, Debo Li, Dong Hai, Wenshuai Chen, Fangfang Du, Licheng Jiao

  KEYWORDS: Polarimetric Synthetic Aperture Radar, Eigenvalue, Mahalanobis Distance, Spectral Clustering, Image Segmentation

  JOURNAL NAME: Journal of Geographic Information System, Vol.10 No.1, February 26, 2018

  ABSTRACT: A simple and fast approach based on eigenvalue similarity metric for Polarimetric SAR image segmentation of Land Cover is proposed in this paper. The approach uses eigenvalues of the coherency matrix as to construct similarity metric of clustering algorithm to segment SAR image. The Mahalanobis distance is used to metric pairwise similarity between pixels to avoid the manual scale parameter tuning in previous spectral clustering method. Furthermore, the spatial coherence constraints and spectral clustering ensemble are employed to stabilize and improve the segmentation performance. All experiments are carried out on three sets of Polarimetric SAR data. The experimental results show that the proposed method is superior to other comparison methods.