Scattering-Based SARBM3D

Martino, Gerardo Di; Simone, Alessio Di; Iodice, Antonio; Poggi, Gianfranco; Riccio, Daniele; Verdoliva, Luisa

doi:10.1109/jstars.2016.2543303

Cited by 36 publications

(25 citation statements)

References 44 publications

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“…Compared with MP, OMP also converges faster with the same precision. In this paper, we adopt OMP to solve the sparse model in Equation (9). Additionally, since the over-completed dictionary D in the aforementioned model is fixed, it may make the image edge blur, produce Gibbs phenomenon, and so on when de-noising the image.…”

Section: Shift Invariant K-svdmentioning

confidence: 99%

“…Finally, the de-noised image can be obtained by the inverse transform. For example, SAR image de-noising based on non-local similar block matching in NSST domain in [7], synthetic aperture radar image de-noising based shearlet transform using the context-based model in [8], and scattering-based SAR-Block Matching 3D (SARBM3D) [9]. Common transforms include Fourier transform, discrete cosine transform (DCT), wavelet and multi-scale multi-directional transform [10].…”

Section: Introductionmentioning

confidence: 99%

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SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Liu

2017

Remote Sensing

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Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we present one new SAR image de-noising method based on shift invariant K-SVD and guided filter. The whole method consists of two steps. The first deals mainly with the noisy image with shift invariant K-SVD and obtaining the initial de-noised image. In the second step, we do the guided filtering for the initial de-noised image. Finally, we can recover the final de-noised image. Experimental results show that our method not only has better visual effects and objective evaluation, but can also save more detailed information such as image edge and texture when de-noising SAR images. The presented shift invariant K-SVD can be widely used in image processing, such as image fusion, edge detection and super-resolution reconstruction.

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Section: Shift Invariant K-svdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Liu

2017

Remote Sensing

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“…Besides topographic mapping, it is here demonstrated that range slopes can be conveniently applied to SAR despeckling. To this aim, local slopes are exploited for the estimation of the local incidence angle, which in turn is exploited in the SB-PPB filter, a recent despeckling algorithm based on scattering [30][31][32]. Such algorithms introduce physical insights in conventional despeckling by injecting a priori scattering information about the illuminated surface in pre-existing filters.…”

Section: Introductionmentioning

confidence: 99%

Fractal-Based Local Range Slope Estimation from Single SAR Image with Applications to SAR Despeckling and Topographic Mapping

2018

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In this paper, we propose a range slope estimation procedure from single synthetic aperture radar (SAR) images with both methodological and applicative innovations. The retrieval algorithm is based on an analytical linearized direct model, which relates the SAR intensity data to the range local slopes and encompasses both a surface model and an electromagnetic scattering model. Scene topography is described via fractal geometry, whereas the Small Perturbation Method is adopted to represent the scattering behavior of the surface. The range slope map is then used to estimate the surface topography and the local incidence angle map. For topographic mapping applications, also referred to as shape from shading, a regularization procedure is derived to recover the azimuth local slope and reduce distortions. Then we present a new intriguing application of the inversion procedure in the field of SAR despeckling. Proposed techniques and high-level products are tested in a wide series of experiments, where the algorithms are applied to both simulated (canonical) and actual SAR images. It is proved that the proposed range slope retrieval technique can (1) provide an estimate of the surface shape, with overall better performance w.r.t. typical models used in this field and (2) be useful in advanced despeckling techniques.

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“…Starting with the simple spatial multilook approach, numerous techniques and approaches have been developed so far Deledalle et al (2009), Parrilli et al (2012), Di Martino et al (2015), Di Martino et al (2016), Di Martino et al (2016a). Most techniques are based on the straightforward idea of averaging similar pixels (or patches, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…the scattering mechanisms, involved in SAR imagery. The authors recently introduced a novel scattering-based despeckling algorithm Di Martino et al (2015) and applied scattering concepts to PPB Di Martino et al (2016) and SARBM3D Di Martino et al (2016a). It is noticeable that, at least in principle, any despeckling algorithm coping with SAR intensity models could take advantage of scattering issues, inserting physicalbased concepts into the filter.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of a scattering-based nonlocal means Despeckling Algorithm

Simone

Martino

Iodice

et al. 2017

European Journal of Remote Sensing

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Synthetic Aperture Radar (SAR) images are greatly affected by the speckle noise. In order to improve SAR data readability by human interpreters and information extraction performed by computer programs, a despeckling preprocessing step is mandatory. The authors recently presented a despeckling algorithm based on the a priori knowledge of the local topography. In this paper, an experimental sensitivity analysis of the aforementioned despeckling algorithm is conducted and the main results are discussed. In particular, the sensitivity of the filter against surface parameters and scattering behavior is analyzed. A comprehensive understanding of the role of the Digital Elevation Model resolution and the coregistration step is also provided. ARTICLE HISTORY

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Scattering-Based SARBM3D

Cited by 36 publications

References 44 publications

SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Fractal-Based Local Range Slope Estimation from Single SAR Image with Applications to SAR Despeckling and Topographic Mapping

Sensitivity analysis of a scattering-based nonlocal means Despeckling Algorithm

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