A multi-level segmentation methodology for dual-polarized SAR data

An innovative methodology for dual-polarized Synthetic Aperture Radar (SAR) data segmentation is proposed. The methodology is based on the thresholding of the 1D-histograms of the two images produced by the dual polarimetric bands. Thresholding of the histograms is performed using a nonparametric algorithm. Histograms after thresholding are combined together in a two dimensional histogram-based space in order to define sub-spaces, which are used for image segmentation. Sub-spaces are further divided based on two criteria which lead to a multi-level segmentation approach. Dual-polarized TerraSAR-X data, both HH and VV, are used in a study area located in the southwestern United Kingdom.

Research highlights
► New nonparametric image segmentation for dual polarized SAR data.
► The proposed methodology acts globally while the split-merge algorithm acts locally.
► Topological relationships in the proposed methodology are produced in the two dimensional histogram-based space. In the split-merge algorithm topological relationships are produced in the two dimensional space of the image.