Optimal Parameter Estimation in Heterogeneous Clutter for High-Resolution Polarimetric SAR Data

OCEANS 2016 MTS/IEEE Monterey

d'Urso²,

Goarant³

et al. 2016

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Section: Active Ultrasound Monitoring Systemmentioning

confidence: 99%

Potential of active ultrasound monitoring systems for jellyfish detection

OCEANS 2016 MTS/IEEE Monterey

d'Urso²,

Goarant³

et al. 2016

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“…Most methods described in the literature focus on the Hermitian, semidefinite positive coherence or covariance matrix [4]. Nevertheless, the investigation of higher order moments has recently sparked great interest of the SAR community, introducing supplementary information to the clutter analysis and consequently leading to new ICTD approaches [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Information extraction by blind source separation from polarimetric SAR data

Pralon

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Mura

et al. 2017

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Cloude and Pottier H/α feature space [1] is one of the most employed methods for unsupervised PolSAR data classification based on Incoherent Target Decomposition. The association of the coherence matrix eigenvectors to the most dominant scatters in the analysed pixel introduces unfeasible regions in the H/α plane. The Independent Component Analysis provides promising new information to better interpret non-Gaussian heterogeneous clutter in the frame of polarimetric incoherent target decompositions. Not constrained to any orthogonality between the estimated scattering mechanisms that compose the clutter under analysis, ICA does not introduce any unfeasible region in the H/α plane, increasing the range of possible natural phenomenons depicted in the aforementioned feature space.

“…Furthermore, it has an intrinsic property that the derived scatters are orthogonal and uncorrelated, which for Gaussian clutters also means independence. The drawback of this kind of method emerge when the clutter is not Gaussian [5,6] or not composed by orthogonal mechanisms, situations where the performance of the algorithm could be compromised.…”

Section: Introductionmentioning

confidence: 99%

Blind source separation in polarimetric SAR interferometry

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Pralon

2016

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Polarimetric incoherent target decomposition aims in accessing physical parameters of illuminated scatters through the analysis of target coherence or covariance matrix. In this framework, Independent Component Analysis (ICA) was recently proposed as an alternative method to Eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high resolution SAR systems). Until now, the two main drawbacks reported of the aforementioned method are the greater number of samples required for an unbiased estimation, when compared to classical Eigenvector decomposition and the inability to be employed in scenarios under Gaussian clutter assumption. First, a Monte Carlo approach is performed in order to investigate the bias in estimating the Touzi Target Scattering Vector Model (TSVM) parameters when ICA is employed. A RAMSES X-band image acquired over Brétigny, France is taken into consideration to investigate the bias estimation under different scenarios. Finally, some results in terms of POLinSAR coherence optimization [1] in the context of ICA are proposed.