MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?

Deledalle, Charles-Alban; Denis, Loïc; Tabti, Sonia; Tupin, Florence

doi:10.1109/tip.2017.2713946

Cited by 128 publications

(113 citation statements)

References 69 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…A suggestion would be to define a set of standardized test cases for interferogram filtering available to everyone that allows for the assessment and comparison of the performance of algorithms in the most relevant typical situations. Steps in this direction have already been taken by the authors of [115,117,118] by providing downloadable code for their algorithms and by [117] by also providing part of their test data.…”

Section: Discussionmentioning

confidence: 99%

“…An interesting new option is proposed in [118] under the name multichannel logarithm with Gaussian denoising (MuLoG). It transforms the field of sample covariance matrices of a stack of multichannel SAR data in such a way that denoising algorithms for additive white noise are applicable.…”

mentioning

confidence: 99%

“…DDID and BM3D show small oscillatory artefacts. The open source code for MuLoG is available (see [118]).…”

mentioning

confidence: 99%

See 2 more Smart Citations

InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances

Even

Schulz

2018

Remote Sensing

View full text Add to dashboard Cite

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique able to measure deformation of the earth's surface over large areas. InSAR deformation analysis uses two main categories of backscatter: Persistent Scatterers (PS) and Distributed Scatterers (DS). While PS are characterized by a high signal-to-noise ratio and predominantly occur as single pixels, DS possess a medium or low signal-to-noise ratio and can only be exploited if they form homogeneous groups of pixels that are large enough to allow for statistical analysis. Although DS have been used by InSAR since its beginnings for different purposes, new methods developed during the last decade have advanced the field significantly. Preprocessing of DS with spatio-temporal filtering allows today the use of DS in PS algorithms as if they were PS, thereby enlarging spatial coverage and stabilizing algorithms. This review explores the relations between different lines of research and discusses open questions regarding DS preprocessing for deformation analysis. The review is complemented with an experiment that demonstrates that significantly improved results can be achieved for preprocessed DS during parameter estimation if their statistical properties are used.on DInSAR is given in [2]). The initial approach consisted of using three images that were used to form interferograms and a DEM. The DEM served to calculate and remove the synthetic phase. Furthermore, phase unwrapping was applied to generate the DEM and to obtain the deformation field. An algorithm for phase unwrapping was developed somewhat earlier for DEM generation from InSAR data in [9]. During the following years, techniques for most of the basic challenges of InSAR were developed. Early work on phase statistics and the phenomenon of decorrelation can be found in [10][11][12][13][14][15]. Enhancement of signal quality by filtering was considered, e.g., in [16,17]. Investigations, where stacking of interferograms is used to mitigate atmospheric delay, are discussed in [18][19][20]. Theory and algorithms were based on DS until the existence of PS was observed in the late 1990s by Ferretti while working on DEM reconstruction from a stack of SAR images [21]. In the sequel, they developed the first Persistent Scatterer Interferometry (PSI) algorithm [22,23], which extended the applicability of InSAR to scenes where enough PS are found but large parts are strongly decorrelated and hence unwrapping on the full interferograms cannot succeed. For several years from this time on, DInSAR and PSI developed in parallel. The next big step for DInSAR was the small-baseline subset (SBAS) technique [24]. By considering a redundant graph of small baseline interferograms, the effects of decorrelation could be mitigated and the redundancy enhanced robustness of estimation. In this first version, only DS were considered (using boxcar multilooking), but the next step [25] was to include processing of PS: coherent targets in the full resolution interferograms were recognized as having small r...

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances

Even

Schulz

2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In this paper, the chosen filtering methods include (a) the refined Lee filter; (b) the iterative weight bilateral (ite-bilateral) filter according to the iterative filter weight of the spatial and polarimetric distances; (c) the nonlocal Lee (NL-Lee) filter based on the combination of the test statistics and the preselection; (d) the speckle reducing anisotropic diffusion (SRAD) filter by utilizing the partial differential equations [35][36][37][38]; (e) the MuLog-TV filter of the latest algorithm applying Gaussian denoisers [39]; and (f) the proposed CVPB-NLM filter method.…”

Section: Evaluation and Comparisionmentioning

confidence: 99%

Feature-Based Nonlocal Polarimetric SAR Filtering

et al. 2017

View full text Add to dashboard Cite

Polarimetric synthetic aperture radar (PolSAR) images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance (CV) and Pauli basis (PB) to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.

show abstract

“…Many speckle reduction methods have been proposed in the recent reviews [14,15]. To reduce the influence of speckle and improve the efficiency of using SAR images, we apply multi-channel logarithm with Gaussian denoising (MuLoG) to process all SAR images [16]. In the comparison step, a suitable dissimilarity measure is used to compute the distance of two images.…”

Section: Introductionmentioning

confidence: 99%

K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series

et al. 2019

View full text Add to dashboard Cite

In this paper, we present a novel method for change-pattern mining in Synthetic Aperture Radar (SAR) image time series based on a distance matrix clustering algorithm, called K-Matrix. As it is different from the state-of-the-art methods, which analyze the SAR image time series based on the change detection matrix (CDM), here, we directly use the distance matrix to determine changed pixels and extract change patterns. The proposed scheme involves two steps: change detection in SAR image time series and change-pattern discovery. First, these distance matrices are constructed for each spatial position over the time series by a dissimilarity measurement. The changed pixels are detected by using a thresholding algorithm on the energy feature map of all distance matrices. Then, according to the change detection results in SAR image time series, the changed areas for pattern mining are determined. Finally, the proposed K-Matrix algorithm which clusters distance matrices by the matrix cross-correlation similarity is used to group all changed pixels into different change patterns. Experimental results on two datasets of TerraSAR-X image time series illustrate the effectiveness of the proposed method.

show abstract

MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction?

Cited by 128 publications

References 69 publications

InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances

InSAR Deformation Analysis with Distributed Scatterers: A Review Complemented by New Advances

Feature-Based Nonlocal Polarimetric SAR Filtering

K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series

Contact Info

Product

Resources

About