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.
Highlights
The fully-polarimetric synthetic aperture radar (SAR) [1] [2] [3] has the ability to provide information in four channels HH, HV, VH and VV, and contains complete polarization information of electromagnetic waves’ effect on surface
A simple and fast approach based on eigenvalue similarity metric for Polarimetric SAR image segmentation of Land Cover is proposed in this paper
This paper introduces an approach for segmentation of the POLSAR data based on eigenvalue similarity metric
Summary
The fully-polarimetric synthetic aperture radar (SAR) [1] [2] [3] has the ability to provide information in four channels HH, HV, VH and VV, and contains complete polarization information of electromagnetic waves’ effect on surface. The choice of distance depends on the characteristics of the object, and it is generally applied to cluster as similarity metric. In the used POL-SAR segmentation, according to the coherency matrix which is obeying the Wishart distribution, Anfinsen [20] and Ersahin [21] have chosen the Wishart distance with Gaussian kernel as the similarity metric, and applied it to spectral clustering. This kind of special distribution of the T-matrix limits the construction of similarity metric. In order to improve and stabilize the segmentation results, the strategy of cluster ensemble is used
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