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Remote Sens. 2012, 4(6), 1559-1572; doi:10.3390/rs4061559

Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling

1,* , 1
1 Faculty of Engineering, Niigata University, 8050 Ikarashi 2-no-cho, Nishi-ku, Niigata 950-2108, Japan 2 Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
* Author to whom correspondence should be addressed.
Received: 10 April 2012 / Revised: 21 May 2012 / Accepted: 22 May 2012 / Published: 29 May 2012
(This article belongs to the Special Issue Remote Sensing by Synthetic Aperture Radar Technology)
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In this paper, the three-component power decomposition for polarimetric SAR (PolSAR) data with an adaptive volume scattering model is proposed. The volume scattering model is assumed to be reflection-symmetric but parameterized. For each image pixel, the decomposition first starts with determining the adaptive parameter based on matrix similarity metric. Then, a respective scattering power component is retrieved with the established procedure. It has been shown that the proposed method leads to complete elimination of negative powers as the result of the adaptive volume scattering model. Experiments with the PolSAR data from both the NASA/JPL (National Aeronautics and Space Administration/Jet Propulsion Laboratory) Airborne SAR (AIRSAR) and the JAXA (Japan Aerospace Exploration Agency) ALOS-PALSAR also demonstrate that the proposed method not only obtains similar/better results in vegetated areas as compared to the existing Freeman-Durden decomposition but helps to improve discrimination of the urban regions.
Keywords: polarimetric SAR; power decomposition; adaptive volume scattering model polarimetric SAR; power decomposition; adaptive volume scattering model
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Cui, Y.; Yamaguchi, Y.; Yang, J.; Park, S.-E.; Kobayashi, H.; Singh, G. Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling. Remote Sens. 2012, 4, 1559-1572.

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