Classification-based nonlocal SAR despeckling

Gragnaniello, Diego; Poggi, Gianfranco; Verdoliva, Luisa

doi:10.1109/tywrrs.2012.6381115

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…In these cases, the iterative filtering ensured slow convergence to the original value. To resolve this problem, the expression inside tanh in (17) has been normalized by the parameter C…”

Section: B Motivations and The Proposed Improved Iterative Filtermentioning

confidence: 99%

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Enhancement of SAR Speckle Denoising Using the Improved Iterative Filter

Yahia

Ali

Mortula

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The recent advancement in synthetic aperture radar (SAR) technology has enabled high-resolution imaging capability that calls for efficient speckle filtering algorithms to preprocess radar imagery. Since the introduction of the Lee sigma filter in 1980, the various versions of the minimum mean square error (MMSE) filter were developed, focusing essentially on how to estimate the processed pixels. For instance, the iterative MMSE (IMMSE) filter that is commonly initialized by the boxcar filter maintains the initially filtered homogeneous areas and corrects the initially blurred spatial details after a few iterations. In this article, an effort is made to enhance the performance of the IMMSE filter in terms of speckle reduction and spatial detail preservation by refining the choice of the initial filter, optimizing its parameters, and improving the estimation of local statistics. Compared with the basic version, results showed that the improved iterative filter considerably enhanced the filtering criteria. When the improved iterative filtering process was initialized by the nonlocal mean filter, for few iterations, the filtering performances were improved. Simulated, airborne (ESAR, Oberpfaffenhofen Germany) and spaceborne (Sentinel 1, Palm Jumeirah Dubai UAE) SAR data were used to assess the filtering performances of the studied filters. Index Terms-Iterative minimum mean square error (IMMSE) filter, nonlocal means filter, synthetic aperture radar (SAR), speckle. I. INTRODUCTION T HE synthetic aperture radar (SAR) presents many benefits over optical remote sensing [1], principally the all-day and all-weather acquisition capability. SAR images have been used for a wide range of applications, such as disaster monitoring and forestry, agriculture. However, SAR images are affected by Manuscript

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“…In these cases, the iterative filtering ensured slow convergence to the original value. To resolve this problem, the expression inside tanh in (17) has been normalized by the parameter C…”

Section: B Motivations and The Proposed Improved Iterative Filtermentioning

confidence: 99%

“…Also, several techniques based on a Bayesian NL framework have been developed for denoising SAR images [15]. The NL principle has been successfully used to despeckle in the wavelet domain [16], [17]. Martino et al [18] extended the block This work is licensed under a Creative Commons Attribution 4.0 License.…”

mentioning

confidence: 99%

Enhancement of SAR Speckle Denoising Using the Improved Iterative Filter

Yahia

Ali

Mortula

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Other approaches consider a Bayesian NL framework [113], which has been applied to the despeckling of both ultrasound images [114] and SAR images [115]. The NL principle has been successfully applied also to despeckling in the wavelet domain [109], [116]. Namely, in [109] the authors extend the BM3D filter by redefining the similarity measure among block of pixels according to [108], and employing the LLMMSE principle [78] in the estimation step.…”

Section: Non-bayesian Approachesmentioning

confidence: 99%

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Argenti

Lapini

Bianchi

et al. 2013

IEEE Geosci. Remote Sens. Mag.

418

209

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Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial-and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the cost-performance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain methods.

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“…This straightforward action has motivations that go beyond efficiency, as observed in [13]. Nonlocal techniques, in fact, compare very well with local filtering when dealing with edged/textured blocks, since they are able to find blocks that share an underlying signal similar to the target's, and exploit such similarities.…”

Section: A Variable-size Search Areamentioning

confidence: 99%

Fast Adaptive Nonlocal SAR Despeckling

Cozzolino

Parrilli

Scarpa

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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164

101

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Despeckling techniques based on the nonlocal approach provide an excellent performance, but exhibit also a remarkable complexity, unsuited to time-critical applications. In this letter, we propose a fast nonlocal despeckling filter. Starting from the recent SAR-BM3D algorithm, we propose to use a variable-size search area driven by the activity level of each patch, and a probabilistic early termination approach that exploits speckle statistics in order to speed up block matching. Finally, the use of look-up tables helps in further reducing the processing costs. The technique proposed conjugates excellent performance and low complexity, as demonstrated on both simulated and real-world SAR images and on a dedicated SAR despeckling benchmark.

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Classification-based nonlocal SAR despeckling

Cited by 10 publications

References 21 publications

Enhancement of SAR Speckle Denoising Using the Improved Iterative Filter

Enhancement of SAR Speckle Denoising Using the Improved Iterative Filter

A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images

Fast Adaptive Nonlocal SAR Despeckling

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