Employing a Mixture of Rayleigh and Inverse Gaussian Distributions as SAR Clutter Texture Model in MAP Estimation for Efficient Speckle Suppression
This article proposes a speckle suppression technique based on a binary mixture of the Rayleigh and the reciprocal of Gaussian (RIG) distributions. This model suitably characterizes the texture of synthetic aperture radar (SAR) return from regions with varying degrees of roughness and captures the m...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11002505/ |
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| author | Dheeren Ku Mahapatra Saurav Gupta Biswajit Jena Ravi Prakash Dwivedi |
| author_facet | Dheeren Ku Mahapatra Saurav Gupta Biswajit Jena Ravi Prakash Dwivedi |
| author_sort | Dheeren Ku Mahapatra |
| collection | DOAJ |
| description | This article proposes a speckle suppression technique based on a binary mixture of the Rayleigh and the reciprocal of Gaussian (RIG) distributions. This model suitably characterizes the texture of synthetic aperture radar (SAR) return from regions with varying degrees of roughness and captures the multimodal behavior observed in extremely heterogeneous SAR clutter. We estimate the RIG mixture model parameters by maximum likelihood (ML) with the expectation maximization (EM) algorithm. We also obtain the Crámer-Rao Bounds (CRBs) for these estimators. Finally, we propose a maximum-a-posteriori (MAP) estimator for efficient despeckling by utilizing the RIG model as a prior distribution for the texture component. The accuracy of RIG-MAP estimation for texture is performed on single-look clutter data from actual sensors and multilook simulated clutter data. Qualitative and quantitative results on despeckling illustrate the effectiveness of the proposed MAP estimator in suppressing speckle while preserving mean, textural information, fine details, etc. Furthermore, the RIG-MAP estimator achieves superior performance compared to MMSE (minimum mean square error) - based Lee filter, Kuan filter, and MAP-based (such as <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-MAP, <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula>-MAP, CR-MAP, <inline-formula> <tex-math notation="LaTeX">$\mathcal {G}^{0}$ </tex-math></inline-formula>-MAP) estimators. |
| format | Article |
| id | doaj-art-a79a67c0b3dc4158af2f4434cd4e5849 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a79a67c0b3dc4158af2f4434cd4e58492025-08-20T01:53:04ZengIEEEIEEE Access2169-35362025-01-0113882558826710.1109/ACCESS.2025.356941711002505Employing a Mixture of Rayleigh and Inverse Gaussian Distributions as SAR Clutter Texture Model in MAP Estimation for Efficient Speckle SuppressionDheeren Ku Mahapatra0https://orcid.org/0000-0002-8151-1552Saurav Gupta1https://orcid.org/0000-0001-8028-547XBiswajit Jena2https://orcid.org/0000-0002-2659-3364Ravi Prakash Dwivedi3https://orcid.org/0000-0002-5946-8690School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaCentre for Nanoelectronics and VLSI Design, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, IndiaThis article proposes a speckle suppression technique based on a binary mixture of the Rayleigh and the reciprocal of Gaussian (RIG) distributions. This model suitably characterizes the texture of synthetic aperture radar (SAR) return from regions with varying degrees of roughness and captures the multimodal behavior observed in extremely heterogeneous SAR clutter. We estimate the RIG mixture model parameters by maximum likelihood (ML) with the expectation maximization (EM) algorithm. We also obtain the Crámer-Rao Bounds (CRBs) for these estimators. Finally, we propose a maximum-a-posteriori (MAP) estimator for efficient despeckling by utilizing the RIG model as a prior distribution for the texture component. The accuracy of RIG-MAP estimation for texture is performed on single-look clutter data from actual sensors and multilook simulated clutter data. Qualitative and quantitative results on despeckling illustrate the effectiveness of the proposed MAP estimator in suppressing speckle while preserving mean, textural information, fine details, etc. Furthermore, the RIG-MAP estimator achieves superior performance compared to MMSE (minimum mean square error) - based Lee filter, Kuan filter, and MAP-based (such as <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-MAP, <inline-formula> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula>-MAP, CR-MAP, <inline-formula> <tex-math notation="LaTeX">$\mathcal {G}^{0}$ </tex-math></inline-formula>-MAP) estimators.https://ieeexplore.ieee.org/document/11002505/SAR clutter amplitudespeckleRayleigh-inverse Gaussian mixture modelexpectation-maximization |
| spellingShingle | Dheeren Ku Mahapatra Saurav Gupta Biswajit Jena Ravi Prakash Dwivedi Employing a Mixture of Rayleigh and Inverse Gaussian Distributions as SAR Clutter Texture Model in MAP Estimation for Efficient Speckle Suppression IEEE Access SAR clutter amplitude speckle Rayleigh-inverse Gaussian mixture model expectation-maximization |
| title | Employing a Mixture of Rayleigh and Inverse Gaussian Distributions as SAR Clutter Texture Model in MAP Estimation for Efficient Speckle Suppression |
| title_full | Employing a Mixture of Rayleigh and Inverse Gaussian Distributions as SAR Clutter Texture Model in MAP Estimation for Efficient Speckle Suppression |
| title_fullStr | Employing a Mixture of Rayleigh and Inverse Gaussian Distributions as SAR Clutter Texture Model in MAP Estimation for Efficient Speckle Suppression |
| title_full_unstemmed | Employing a Mixture of Rayleigh and Inverse Gaussian Distributions as SAR Clutter Texture Model in MAP Estimation for Efficient Speckle Suppression |
| title_short | Employing a Mixture of Rayleigh and Inverse Gaussian Distributions as SAR Clutter Texture Model in MAP Estimation for Efficient Speckle Suppression |
| title_sort | employing a mixture of rayleigh and inverse gaussian distributions as sar clutter texture model in map estimation for efficient speckle suppression |
| topic | SAR clutter amplitude speckle Rayleigh-inverse Gaussian mixture model expectation-maximization |
| url | https://ieeexplore.ieee.org/document/11002505/ |
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