Adaptive Model-Based Decomposition of Polarimetric SAR Covariance Matrices

Arii, Motofumi; Zyl, J.J. van; Kim, Yun-Jin

doi:10.1109/tgrs.2010.2076285

Cited by 225 publications

(145 citation statements)

References 12 publications

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“…By doing so, the SAVSM depends only on one unknown parameter rather than two in Arii model. Another one is the generalized volume scattering model (GVSM) [11] proposed by Antropov et al after considering geophysical media symmetry.…”

Section: Introductionmentioning

confidence: 99%

A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

Xie

Zhu

López-Sánchez

et al. 2018

IEEE Geosci. Remote Sensing Lett.

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Section: Introductionmentioning

confidence: 99%

A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

Xie

Zhu

López-Sánchez

et al. 2018

IEEE Geosci. Remote Sensing Lett.

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“…In order to improve the model-fitting performance in the polarimetric decomposition, several recent studies have focused on improving the estimation accuracy of the volume scattering power [16][17][18][19]. However, experimental results in this study demonstrate that, even when the volume scattering power is successfully estimated, the ground-related scattering components can be significantly misestimated due to the topographic slope.…”

Section: Discussionmentioning

confidence: 87%

The Effect of Topography on Target Decomposition of Polarimetric SAR Data

Park

2015

Remote Sensing

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Polarimetric target decomposition enables the interpretation of radar images more easily, mostly based on physical assumptions, i.e., fitting physically-based scattering models to the polarimetric SAR observations. However, the model-fitting result cannot be always successful. Particularly, the performance of model-fitting in sloping forests is still an open question. In this study, the effect of ground topography on the model-fitting-based polarimetric decomposition techniques is investigated. The estimation accuracy of each scattering component in the decomposition results are evaluated based on the simulated target matrix by using the incoherent vegetation scattering model that accounts for the tilted scattering surface beneath the forest canopy. Experimental results show that the surface and the double-bounce scattering components can be significantly misestimated due to the topographic slope, even when the volume scattering power is successfully estimated.

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“…Moreover, the adaptive decomposition technique goes one step further for modification of the covariance matrix of the volume (canopy) component by characterizing it with a mean orientation angle and a degree of randomness. This decomposition model [17] can be written as…”

Section: Amd For Polarimetric Sar Datamentioning

confidence: 99%

“…According to the derivation in [17], the mean orientation angle θ 0 varies from 0 to π and the degree of randomness σ from 0 to 0.91. We conduct adaptive decomposition according to the following procedure:…”

Section: Amd For Polarimetric Sar Datamentioning

confidence: 99%

“…Furthermore, the decomposition may result in many pixels with a negative power. Arii et al [17] suggested an adaptive model-based decomposition (AMD) technique in which no reflection symmetry is required. We use this decomposition in our classification algorithm instead of Freeman decomposition to overcome the flaws introduced by their assumptions.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Polinsar Unsupervised Classification Based on Adaptive Model-Based Decomposition and Shannon Entropy Characterization

Song¹,

Yang²,

Xu³

et al. 2013

PIER B

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Abstract-We introduce a data-driven unsupervised classification algorithm that uses polarimetric and interferometric synthetic aperture radar (PolInSAR) data. The proposed algorithm uses a classification method that preserves scattering characteristics. Our contribution is twofold.First, the method applies adaptive model-based decomposition (AMD) to represent the scattering mechanism, which overcomes the flaws introduced by Freeman decomposition. Second, a new class initialization scheme using a histogram clustering algorithm based on a Dirichlet process mixture model is applied to automatically determine the number of clusters and effectively initialize the classes. Therefore, our algorithm is data-driven. In the first step, the Shannon entropy characteristics of the PolInSAR data are extracted and used to calculate the local histogram features. After applying AMD, pixels are divided into three canonical scattering categories according to their dominant scattering mechanism. The histogram clustering algorithm is applied to each scattering category to obtain the number of classes and initialize them. The iterative Wishart classifier is applied to refine the classification results. Experimental results for E-SAR L-band PolInSAR images from the German Aerospace Center demonstrate the effectiveness of the proposed algorithm.

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Adaptive Model-Based Decomposition of Polarimetric SAR Covariance Matrices

Cited by 225 publications

References 12 publications

A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

A Modified General Polarimetric Model-Based Decomposition Method With the Simplified Neumann Volume Scattering Model

The Effect of Topography on Target Decomposition of Polarimetric SAR Data

Data-Driven Polinsar Unsupervised Classification Based on Adaptive Model-Based Decomposition and Shannon Entropy Characterization

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