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.