Perturbation Analysis of Eigenvector-Based Target Decomposition Theorems in Radar Polarimetry

López-Martínez, Carlos; Alonso-González, Alberto; Fábregas, X.

doi:10.1109/tgrs.2013.2257802

Cited by 14 publications

(25 citation statements)

References 23 publications

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“…, E) by 2 × 4, 3 × 6, 4 × 8 and 5 × 10 boxcar filter. As demonstrated in [18][19][20][21][22], we observe from Figs. 2(a) and (b) that sample entropy was always underestimated.…”

Section: Characterization Of Multilook Effects On the Eigendecompositsupporting

confidence: 60%

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BIAS COMPENSATION IN H/a/Α POLARIMETRIC SAR DECOMPOSITION AND ITS IMPLICATION FOR THE CLASSIFICATION

Yahia¹,

Khalfa²,

Chabir³

et al. 2016

PIER B

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Abstract-Classification of land cover types is one important application of polarimetric synthetic aperture radar (PolSAR) remote sensing. There are numerous features that can be extracted from PolSAR images. Among them, eigenvalues λ i , entropy H, alpha angle α, and anisotropy A are effective and popular tools for the analysis and quantitative estimation of the physical parameters. Nevertheless, the speckle noise appearing in PolSAR images reduces the accuracy of image classification. Consequently, it should be filtered correctly. Generally, filtering PolSAR data generate biased estimates of λ i /H/A/α parameters. In this paper, we studied the effects of bias compensation on supervised and unsupervised PolSAR image classification. We applied the asymptotic quasi maximum likelihood estimator AQ-MLE and Yahia/Aguili's bias compensation methods. To improve the classification accuracies, we demonstrated that bias compensation must be associated with speckle reduction. The combination of the span with biased parameters reduced the effects of bias but did not eliminate it totally. Simulated and real data were used for validation.

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“…, E) by 2 × 4, 3 × 6, 4 × 8 and 5 × 10 boxcar filter. As demonstrated in [18][19][20][21][22], we observe from Figs. 2(a) and (b) that sample entropy was always underestimated.…”

Section: Characterization Of Multilook Effects On the Eigendecompositsupporting

confidence: 60%

“…This technique requires the value of entropy for an infinite number of looks. This shortcoming was surmounted in [21]. However, the main weaknesses of the previous techniques are their dependency on a precise estimation of the independent averaging samples and their amplification of the noise variances [22].…”

Section: Introductionmentioning

confidence: 99%

BIAS COMPENSATION IN H/a/Α POLARIMETRIC SAR DECOMPOSITION AND ITS IMPLICATION FOR THE CLASSIFICATION

Yahia¹,

Khalfa²,

Chabir³

et al. 2016

PIER B

View full text Add to dashboard Cite

show abstract

“…It is known that the entropy is underestimated if the number of samples for ensemble average of the covariance matrix is low [33,46,47]. To mitigate the underestimation of the entropy, we use AQ-MLE of the eigenvalues [33].…”

Section: Asymptotic Quasi Maximum Likelihood Estimator (Aq-mle) Of Thmentioning

confidence: 99%

“…Nevertheless, it may be still difficult to utilize the pdfs of the covariance matrix and the eigenvalue [27,33,46] to derive H noise . Hence, we suggest yet another approach in the following.…”

Section: Correction Of the Bias In The Entropy Induced By Noise For Tmentioning

confidence: 99%

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Estimation of Jones matrix, birefringence and entropy using Cloude-Pottier decomposition in polarization-sensitive optical coherence tomography

Yamanari¹,

Tsuda²,

Kokubun³

et al. 2016

Biomed. Opt. Express

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Estimation of polarimetric parameters has been a fundamental issue to assess biological tissues that have form birefringence or polarization scrambling in polarizationsensitive optical coherence tomography (PS-OCT). We present a mathematical framework to provide a maximum likelihood estimation of the target covariance matrix and its incoherent target decomposition to estimate a Jones matrix of a dominant scattering mechanism, called Cloude-Pottier decomposition, thereby deriving the phase retardation and the optic axis of the sample. In addition, we introduce entropy that shows the randomness of the polarization property. Underestimation of the entropy at a low sampling number is mitigated by asymptotic quasi maximum likelihood estimator. A bias of the entropy from random noises is corrected to show only the polarization property inherent in the sample. The theory is validated with experimental measurements of a glass plate and waveplates, and applied to the imaging of a healthy human eye anterior segment as an image filter.

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Characterization and Mitigation of Radio Frequency Interference in PolSAR Data

2017

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Polarimetric synthetic aperture radar (PolSAR) is a very important instrument for active remote sensing. However, it is common to find that PolSAR echoes are often contaminated by incoherent electromagnetic interference, which is referred to as radio frequency interference (RFI). The analysis of RFI signatures and its influence on PolSAR data seems to be lacking in existing literatures, especially for PolSAR post products, such as the polarimetric decomposition parameters and clustering result. The goal of this paper is to reveal the link between RFI and polarization, as well as to analyze the impact of interference on PolSAR image and its post products. Qualitative and quantitative analyses of the adverse impact of RFI on the real measured NASA/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar data set are illustrated from two perspectives, that is, evaluation of imaging quality and interpretation of scattering mechanisms. The point target response and effective number of looks are evaluated for assessing the distortion to focusing quality. Further, we discussed the characteristics of ultra wideband RFI and proposed a mitigation method using nonnegative matrix factorization along azimuth direction. The experimental results indicate the effectiveness of the proposed method.

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Perturbation Analysis of Eigenvector-Based Target Decomposition Theorems in Radar Polarimetry

Cited by 14 publications

References 23 publications

BIAS COMPENSATION IN H/a/Α POLARIMETRIC SAR DECOMPOSITION AND ITS IMPLICATION FOR THE CLASSIFICATION

BIAS COMPENSATION IN H/a/Α POLARIMETRIC SAR DECOMPOSITION AND ITS IMPLICATION FOR THE CLASSIFICATION

Estimation of Jones matrix, birefringence and entropy using Cloude-Pottier decomposition in polarization-sensitive optical coherence tomography

Characterization and Mitigation of Radio Frequency Interference in PolSAR Data

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