Remote Sens. 2012, 4(8), 2199-2209; https://doi.org/10.3390/rs4082199
Four-Component Scattering Power Decomposition Algorithm with Rotation of Covariance Matrix Using ALOS-PALSAR Polarimetric Data
Department of Computer Science, School of Electrical and Computer Engineering, National Defense Academy, 1-10-20 Hashirimizu, Yokosuka, Kanagawa 239-8686, Japan
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Received: 5 June 2012 / Revised: 11 July 2012 / Accepted: 16 July 2012 / Published: 25 July 2012
(This article belongs to the Special Issue Remote Sensing by Synthetic Aperture Radar Technology)
Abstract
The present study introduces the four-component scattering power decomposition (4-CSPD) algorithm with rotation of covariance matrix, and presents an experimental proof of the equivalence between the 4-CSPD algorithms based on rotation of covariance matrix and coherency matrix. From a theoretical point of view, the 4-CSPD algorithms with rotation of the two matrices are identical. Although it seems obvious, no experimental evidence has yet been presented. In this paper, using polarimetric synthetic aperture radar (POLSAR) data acquired by Phased Array L-band SAR (PALSAR) on board of Advanced Land Observing Satellite (ALOS), an experimental proof is presented to show that both algorithms indeed produce identical results. View Full-TextKeywords:
polarimetric synthetic aperture radar (POLSAR); scattering power decomposition; radar polarimetry; covariance matrix rotation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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MDPI and ACS Style
Sugimoto, M.; Ouchi, K.; Nakamura, Y. Four-Component Scattering Power Decomposition Algorithm with Rotation of Covariance Matrix Using ALOS-PALSAR Polarimetric Data. Remote Sens. 2012, 4, 2199-2209.
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