Adaptive model based on polarimetric decomposition using correlation coefficient in horizontal–vertical and circular basis

Houda Latrache; Mounira Ouarzeddine; Boularbah Souissi

doi:10.1117/1.JRS.11.036006

4 August 2017 Adaptive model based on polarimetric decomposition using correlation coefficient in horizontal–vertical and circular basis

Houda Latrache, Mounira Ouarzeddine, Boularbah Souissi

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 3, 036006 (August 2017). https://doi.org/10.1117/1.JRS.11.036006

Abstract

This paper presents two decomposition schemes for polarimetric synthetic aperture radar data. The proposed schemes intend to overcome the problem of scattering ambiguity and reduce the volume scattering power in oriented urban areas. The first proposed scheme uses an empirical volume model based on the correlation coefficients of the Pauli component in the horizontal–vertical basis, whereas the second one employs a volume model defined on correlation coefficients of the Pauli components expressed in the circular basis. The correlation coefficients are calculated from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The characteristics adopted from these volume models are used to enhance the results of the decomposition schemes. The scattering powers estimated from the proposed methods give promising results compared to existing methods in the literature, particularly in urban areas since all the oriented built-up areas are well discriminated as double or odd bounce scattering. The methods are evaluated using the experimental airborne SAR sensor (E-SAR) PolInSAR L band data acquired on the Oberpfaffenhofen test site in Germany.

Citation Download Citation

Houda Latrache, Mounira Ouarzeddine, and Boularbah Souissi "Adaptive model based on polarimetric decomposition using correlation coefficient in horizontal–vertical and circular basis," Journal of Applied Remote Sensing 11(3), 036006 (4 August 2017). https://doi.org/10.1117/1.JRS.11.036006

Received: 10 February 2017; Accepted: 30 June 2017; Published: 4 August 2017

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