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Hybrid SAR Speckle Reduction Using Complex Wavelet Shrinkage and Non-Local PCA-Based Filtering.


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Farhadiani, Ramin; Homayouni, Saeid ORCID logoORCID: https://orcid.org/0000-0002-0214-5356 et Safari, Abdolreza (2019). Hybrid SAR Speckle Reduction Using Complex Wavelet Shrinkage and Non-Local PCA-Based Filtering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 12 , nº 5. pp. 1489-1496. DOI: 10.1109/JSTARS.2019.2907655.

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In this paper, a new hybrid despeckling method, based on Undecimated Dual-Tree Complex Wavelet Transform (UDT-CWT) using maximum a posteriori (MAP) estimator and non-local Principal Component Analysis (PCA)-based filtering with local pixel grouping (LPG-PCA), was proposed. To achieve a heterogeneous-adaptive speckle reduction, SAR image is classified into three classes of point targets, details, or homogeneous areas. The despeckling is done for each pixel based on its class of information. Logarithm transform was applied to the SAR image to convert the multiplicative speckle into additive noise. Our proposed method contains two principal steps. In the first step, denoising was done in the complex wavelet domain via MAP estimator. After performing UDT-CWT, the noise-free complex wavelet coefficients of the log-transformed SAR image were modeled as a two-state Gaussian mixture model. Furthermore, the additive noise in the complex wavelet domain was considered as a zero-mean Gaussian distribution. In the second step, after applying inverse UDT-CWT, an iterative LPG-PCA method was used to smooth the homogeneous areas and enhance the details. The proposed method was compared with some state-of-the-art despeckling methods. The experimental results showed that the proposed method leads to a better speckle reduction in homogeneous areas while preserving details.

Type de document: Article
Mots-clés libres: Gaussian mixture model; homomorphic transformation; non-local filtering; undecimated dual-tree complex wavelet transform
Centre: Centre Eau Terre Environnement
Date de dépôt: 29 nov. 2019 14:08
Dernière modification: 08 févr. 2022 21:44
URI: https://espace.inrs.ca/id/eprint/9554

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