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  • 标题:Spectral Clustering with Eigenvalue Similarity Metric Method for POL-SAR Image Segmentation of Land Cover
  • 本地全文:下载
  • 作者:Shuiping Gou ; Debo Li ; Dong Hai
  • 期刊名称:Journal of Geographic Information System
  • 印刷版ISSN:2151-1950
  • 电子版ISSN:2151-1969
  • 出版年度:2018
  • 卷号:10
  • 期号:01
  • 页码:150-164
  • DOI:10.4236/jgis.2018.101007
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要: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.
  • 关键词:Polarimetric Synthetic Aperture Radar;Eigenvalue;Mahalanobis Distance;Spectral Clustering;Image Segmentation
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