3D reconstruction of a scene from Synthetic Aperture Radar (SAR) images mainly relies on interferometric measurements, which involve strict constraints on the acquisition process. These last years, progress in deep learning has significantly advanced 3D reconstruction from multiple views in optical imaging, mainly through reconstruction-by-synthesis approaches popularized by Neural Radiance Fields. In this paper, we propose a new inverse rendering method for 3D reconstruction from a few incoherent SAR views, drawing inspiration from optical approaches. First, we introduce a new simplified differentiable SAR rendering model, able to synthetize images from a Digital Surface Model (DSM) and a radar backscattering coefficients map. Then, we introduce a coarse-to-fine strategy to reconstruct the DSM and the map of backscattering coefficients of a SAR scene starting only from a few SAR views. We use a neural field, i.e., a continuous parametric model based on a Multi-Layer Perceptron, to represent the SAR scene. Finally, we demonstrate the surface reconstruction capabilities of our method on synthetic SAR images produced by ONERA’s physically-based EMPRISE® simulator. Our method showcases the potential of exploiting geometric disparities in SAR images and paves the way for multi-sensor data fusion.

Reducing speckle fluctuations in multichannel SAR images is essential in many applications of synthetic aperture radar (SAR) imaging such as polarimetric classification or interferometric height estimation. While single-channel despeckling has widely benefited from the application of deep learning techniques, extensions to multichannel SAR images are much more challenging. This article introduces MuChaPro, a generic framework that exploits existing single-channel despeckling methods. The key idea is to generate numerous single-channel projections, restore these projections, and recombine them into the final multichannel estimate. This simple approach is shown to be effective in polarimetric and/or interferometric modalities. A special appeal of MuChaPro is the possibility to apply a self-supervised training strategy to learn sensor-specific networks for single-channel despeckling.

Synthetic Aperture Radar (SAR) images offer crucial information for studying and monitoring sea ice in the Arctic. Sentinel-1 captures images of the area using an extremely wide swath for reduced revisit time. The backscattered signal from sea ice and open water is often very weak, making it difficult to distinguish from the sensor thermal noise floor. Thermal noise impacts the images by generating a bias and increasing the fluctuations related to speckle phenomenon. Analyzing these images requires both correcting this bias and reducing fluctuations without blurring out the image content. The acquisition of several sub-swaths in a single pass using Terrain Observation with Progressive Scans (TOPS) produces images that exhibit, after compensation for antenna gains, a non-uniform thermal noise floor and strong discontinuities between sub-swaths. Denoising techniques must take these specificities into account to restore the images. This paper introduces a joint approach to remove the thermal noise offset and suppress fluctuations due to speckle and thermal noise. Compensating at once for all these effects largely reduces artifacts at the boundary between sub-swaths. We demonstrate using both numerical simulations and actual Sentinel-1 images that debiased polarimetric reflectivities can be recovered and fluctuations strongly reduced while preserving fine spatial structures.

Synthetic aperture radar (SAR) is a widely used modality for Earth observation, as they provide weather-independent imaging capabilities. However, interpretation of SAR images is difficult due to the speckle phenomenon: fluctuations appear in the image, which are stronger in areas with high radar reflectivity. As a result, many speckle reduction methods have been developed, with deep learning approaches standing out as particularly effective. Our article presents here a deep learning approach with two novel features: the use of an optical image to improve the restoration of a SAR image, while using a self-supervised neural network training.

A self-supervised despeckling approach based on the decomposition of single-look complex SAR images into their real and imaginary components has recently been introduced under the name MERLIN. At its core is the observation that, under Goodman’s fully developed speckle model, the real and imaginary parts of single-channel SAR images areindependent and identically distributed. The paper commented here, PolMERLIN, proposes an extension to multi-channel SAR images such as polarimetric SAR (PolSAR) images. This extension is based on the independence between real and imaginary parts of PolSAR images, but this independence generally does not hold, as shown here.

Synthetic aperture radar (SAR) provides valuable information about the Earth’s surface in all-weather and day-and-night conditions. Due to the inherent presence of speckle phenomenon, a filtering step is often required to improve the performance of downstream tasks. In this article, we focus on dealing with the spatial correlations of speckle, which impacts negatively many of the existing speckle filters. Taking advantage of the flexibility of variational methods based on the plug-and-play (PnP) strategy, we propose to use a Gaussian denoiser trained to restore SAR scenes corrupted by colored Gaussian noise with correlation structures typical of a range of radar sensors. Our approach improves the robustness of PnP despeckling techniques. Experiments conducted on simulated and real polarimetric SAR images show that the proposed method removes speckle efficiently in the presence of spatial correlations without introducing artifacts, with a good level of detail preservation. Our method can be readily applied, without network re-training or fine-tuning, to filter SAR images from various sensors, acquisition modes (SAR, PolSAR, InSAR, PolInSAR), and spatial resolution. The code of the trained models is made freely available at (https://gitlab.telecom-paris.fr/ring/mulog-drunet).

With Synthetic Aperture Radar tomography, it is possible to reconstruct reflectivity profiles in the direction orthogonal to the line-of-sight. When only a small number of interferometric baselines is available, the spatial resolution of profiles produced by beamforming is insufficient. While many iterative algorithms have been proposed in the past years to achieve improved tomographic reconstructions, these methods often require a large computational cost. In this paper we explore the use of a light-weight neural network to dramatically accelerate tomographic reconstruction in anticipation of the deluge of data generated by the future BIOMASS satellite.

Synthetic aperture radar tomographic imaging reconstructs the three-dimensional reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration. In forest areas, a high number of elements backscatter the radar signal within each resolution cell. To reconstruct the vertical reflectivity profile, state-of-the-art techniques perform a regularized inversion implemented in the form of iterative minimization algorithms. We show that light-weight neural networks can be trained to perform this inversion with a single feed-forward pass, leading to fast reconstructions that could better scale to the amount of data provided by the future BIOMASS mission. We train our encoder-decoder network using simulated data and validate our technique on real L-band and P-band data.

Speckle filtering is generally a prerequisite to the analysis of synthetic aperture radar (SAR) images. Tremendous progress has been achieved in the domain of single-image despeckling. Latest techniques rely on deep neural networks to restore the various structures and textures peculiar to SAR images. The availability of time series of SAR images offers the possibility of improving speckle filtering by combining different speckle realizations over the same area. The supervised training of deep neural networks requires ground-truth speckle-free images. Such images can only be obtained indirectly through some form of averaging, by spatial or temporal integration, and are imperfect. Given the potential of very high-quality restoration reachable by multitemporal speckle filtering, the limitations of ground-truth images need to be circumvented. We extend a recent self-supervised training strategy for single-look complex (SLC) SAR images, called MERLIN, to the case of multitemporal filtering. This requires modeling the sources of statistical dependencies in the spatial and temporal dimensions as well as between the real and imaginary components of the complex amplitudes. Quantitative analysis on datasets with simulated speckle indicates a clear improvement of speckle reduction when additional SAR images are included. Our method is then applied to stacks of TerraSAR-X images and shown to outperform competing multitemporal speckle filtering approaches. The code of the trained models and supplementary results are made freely available athttps://gitlab.telecom-paris.fr/ring/multitemporal-merlin/.

This paper presents a semi-guided method to detect lakes in Sentinel-1 SAR data. The proposed approach is an adaptation of the grab-cut framework developed in [1]. Starting from a coarse bounding box around the lake, an accurate segmentation is extracted using a Conditional Random Field formalism and a graph-cut based optimization. Then an extension of this approach to process jointly a stack of multi-temporal data is presented. A temporal regularization term is introduced to control the joint segmentation. The proposed approach is evaluated on Sentinel-1 datasets. Qualitative and quantitative results demonstrate the interest of the proposed framework and its robustness to the initial-ization polygon of the lake.

Reducing speckle and limiting the variations of the physical parameters in Synthetic Aperture Radar (SAR) images is often a key-step to fully exploit the potential of such data. Nowadays, deep learning approaches produce state of the art results in single-image SAR restoration. Nevertheless, huge multi-temporal stacks are now often available and could be efficiently exploited to further improve image quality. This paper explores two fast strategies employing a single-image despeckling algorithm, namely SAR2SAR [1], in a multi-temporal framework. The first one is based on Quegan filter [2] and replaces the local reflectivity pre-estimation by SAR2SAR. The second one uses SAR2SAR to suppress speckle from a ratio image encoding the multi-temporal information under the form of a “super-image”, i.e. the temporal arithmetic mean of a time series. Experimental results on Sentinel-1 GRD data show that these two multi-temporal strategies provide improved filtering results while adding a limited computational cost.

The increasing availability of synthetic aperture radar (SAR) time series creates many opportunities for remote sensing applications, but it can be challenging in terms of amount of data to process. This letter discusses the interest of the geometric mean to average SAR time series. First, the properties of the geometric mean and the arithmetic mean are compared. Then, a speckle-reduction method specifically designed to improve images obtained with the geometric mean is presented. This method is based on an adaptation of the MuLoG framework to take into account the specific distribution of the geometric mean. Finally, applications of this denoised geometric-mean image are presented.

Speckle fluctuations seriously limit the interpretability of synthetic aperture radar (SAR) images. Speckle reduction has thus been the subject of numerous works spanning at least four decades. Techniques based on deep neural networks have recently achieved a new level of performance in terms of SAR image restoration quality. Beyond the design of suitable network architectures or the selection of adequate loss functions, the construction of training sets is of uttermost importance. So far, most approaches have considered a supervised training strategy: the networks are trained to produce outputs as close as possible to speckle-free reference images. Speckle-free images are generally not available, which requires resorting to natural or optical images or the selection of stable areas in long time series to circumvent the lack of ground truth. Self-supervision, on the other hand, avoids the use of speckle-free images. We introduce a self-supervised strategy based on the separation of the real and imaginary parts of single-look complex (SLC) SAR images, called coMplex sElf-supeRvised despeckLINg (MERLIN), and show that it offers a straightforward way to train all kinds of deep despeckling networks. Networks trained with MERLIN take into account the spatial correlations due to the SAR transfer function specific to a given sensor and imaging mode. By requiring only a single image, and possibly exploiting large archives, MERLIN opens the door to hassle-free as well as large-scale training of despeckling networks. The code of the trained models is made freely available athttps://gitlab.telecom-paris.fr/RING/MERLIN.

Deep learning approaches show unprecedented results for speckle reduction in SAR amplitude images. The wide availability of multi-temporal stacks of SAR images can improve even further the quality of denoising. In this paper, we propose a flexible yet efficient way to integrate temporal information into a deep neural network for speckle suppression. Archives provide access to long time-series of SAR images, from which multi-temporal averages can be computed with virtually no remaining speckle fluctuations. The proposed method combines this multi-temporal average and the image at a given date in the form of a ratio image and uses a state-of-the-art neural network to remove the speckle in this ratio image. This simple strategy is shown to offer a noticeable improvement compared to filtering the original image without knowledge of the multi-temporal average.

The speckle phenomenon remains a major hurdle for the analysis of SAR images. The development of speckle reduction methods closely follows methodological progress in the field of image restoration. The advent of deep neural networks has offered new ways to tackle this longstanding problem. Deep learning for speckle reduction is a very active research topic and already shows restoration performances that exceed that of the previous generations of methods based on the concepts of patches, sparsity, wavelet transform or total variation minimization. The objective of this paper is to give an overview of the most recent works and point the main research directions and current challenges of deep learning for SAR image restoration.

This paper presents a despeckling method for Sentinel-1 GRD images based on the recently proposed framework "SAR2SAR": a self-supervised training strategy. Training the deep neural network on collections of Sentinel 1 GRD images leads to a despeckling algorithm that is robust to space-variant spatial correlations of speckle. Despeckled images improve the detection of structures like narrow rivers. We apply a detector based on exogenous information and a linear features detector and show that rivers are better segmented when the processing chain is applied to images pre-processed by our despeckling neural network.

Synthetic Aperture Radar (SAR) tomographic techniques enable the reconstruction of the scene scattering structure along the vertical direction and can provide the temporal evolution of a cloud of reliable points located in the 3D space. The use of Generalized Likelihood Ratio Test approaches have been shown to be effective in selecting reliable multiple scatterers. Recently regularized tomographic methods have been proposed for increasing the density of the recovered scatterers in urban environments. This paper discusses the differences between these two approaches and performs a comparison of reconstruction results obtained from a stack of TerraSAR-X images, in a region of interest located in the city of Paris, France.

Speckle reduction is a necessary step for many applications. Very effective methods have been developed in the recent years for single-image speckle reduction and multi-temporal speckle filtering. However, to reduce the presence of sidelobes around bright targets, SAR images are spectrally weighted and this processing impacts the speckle statistics by introducing spatial correlations. These correlations severely impact speckle reduction methods that require uncorrelated speckle as input. Thus, spatial down-sampling is typically applied to reduce the speckle spatial correlations prior to speckle filtering. To better preserve the spatial resolution, we describe how to correctly resample SAR images and extract bright targets in order to process full-resolution images with speckle-reduction methods.

The availability of multi-temporal stacks of SAR images opens the way to new speckle reduction methods. Beyond mere spatial filtering, the time series can be used to improve the signal-to-noise ratio of structures that persist for several dates. Among multi-temporal filtering strategies to reduce speckle fluctuations, a recent approach has proved to be very effective: ratio-based filtering (RABASAR). This method, developed to reduce the speckle in multi-temporal intensity images, first computes a "mean image" with a high signal-to-noise ratio (a so-called super-image), and then processes the ratio between the multi-temporal stack and the super-image. In this paper, we propose an extension of this approach to polarimetric SAR images. We illustrate its potential on a stack of fully-polarimetric images from RADARSAT-2 satellite.

Speckle reduction has benefited from the recent progress in image processing, in particular patch-based non-local filtering and deep learning techniques. These two families of methods offer complementary characteristics but have not yet been combined. We explore strategies to make the most of each approach.

In the last decades, several approaches for solving the Phase Unwrapping (PhU) problem using multi-channel Interferometric Synthetic Aperture Radar (InSAR) data have been developed. Many of the proposed approaches are based on statistical estimation theory, both classical and Bayesian. In particular, the statistical approaches based on the use of the whole complex multi-channel dataset have turned to be effective. The latter are based on the exploitation of the covariance matrix, which contains the parameters of interest. In this paper, the added value of the Non Local (NL) paradigm within the InSAR multi-channel PhU framework is investigated. The analysis of the impact of NL technique is performed using multi-channel realistic simulated data and X-band data.

Speckle reduction is a major issue for many SAR imaging applications using amplitude, interferometric, polarimetric or tomographic data. This subject has been widely investigated using various approaches. Since a decade, breakthrough methods based on patches have brought unprecedented results to improve the estimation of radar properties. In this paper, we give a review of the different adaptations which have been proposed in the past years for different SAR modalities (mono-channel data like intensity images, multi-channel data like interferometric, tomographic or polarimetric data, or multimodalities combining optic and SAR images), and discuss the new trends on this subject.

The resolution achieved by current synthetic aperture radar (SAR) sensors provides a detailed visualization of urban areas. Spaceborne sensors such as TerraSAR-X can be used to analyze large areas at a very high resolution. In addition, repeated passes of the satellite give access to temporal and interferometric information on the scene. Because of the complex 3-D structure of urban surfaces, scatterers located at different heights (ground, building facade, and roof) produce radar echoes that often get mixed within the same radar cells. These echoes must be numerically unmixed in order to get a fine understanding of the radar images. This unmixing is at the core of SAR tomography. SAR tomography reconstruction is generally performed in two steps: 1) reconstruction of the so-called tomogram by vertical focusing, at each radar resolution cell, to extract the complex amplitudes (a 1-D processing) and 2) transformation from radar geometry to ground geometry and extraction of significant scatterers. We propose to perform the tomographic inversion directly in ground geometry in order to enforce spatial regularity in 3-D space. This inversion requires solving a large-scale nonconvex optimization problem. We describe an iterative method based on variable splitting and the augmented Lagrangian technique. Spatial regularizations can easily be included in this generic scheme. We illustrate, on simulated data and a TerraSAR-X tomographic data set, the potential of this approach to produce 3-D reconstructions of urban surfaces.

In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a “superimage” by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the “superimage”; and 3) computation of the denoised image by remultiplying the denoised ratio by the “superimage.” Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the “superimage” that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (SAR) time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.

Image restoration methods aim to recover the underlying clean image from corrupted observations. The expected patch log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes the EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fast-EPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude over EPLL while incurring a negligible drop in the restored image quality (less than 0.5 dB). We demonstrate the efficacy and versatility of our algorithm on a number of inverse problems, such as denoising, deblurring, super-resolution, inpainting, and devignetting. To the best of our knowledge, the FEPLL is the first algorithm that can competitively restore a pixel image in under 0.5 s for all the degradations mentioned earlier without specialized code optimizations, such as CPU parallelization or GPU implementation.

The search for exoplanet is a very active topic in astronomy. Exoplanet detection by direct imaging requires both dedicated instruments to mask out the host star and careful image processing methods. Data processing is challenging because the exoplanet signal is very faint and hidden in a much stronger non-stationary background displaying strong spatial correlations. In contrast to previous detection methods, we explicitly model the spatial correlations of the background and design a completely unsupervised method that accounts for the background non-stationarity. From a time series of observations, we learn a local model of the distribution of background patches. Significant sources are then detected with a generalized likelihood ratio test. The sub-pixel location and flux of each detected exoplanet are estimated jointly to a refining of the background model. Each detected source is removed from the data, following an orthogonal matching pursuit strategy. The stopping criterion is based on a control of false alarms. We compare the proposed algorithm to three state-of-the-art exoplanet detection methods on datasets obtained with SPHERE instrument operating at the Very Large Telescope (VLT) in Chile. We show a drastic improvement of the sensibility and much fewer false detections.

Speckle reduction is a long-standing topic in SAR data processing. Continuous progress made in the field of image denoising fuels the development of methods dedicated to speckle in SAR images. Adaptation of a denoising technique to the specific statistical nature of speckle presents variable levels of difficulty. It is well known that the logarithm transform maps the intrinsically multiplicative speckle into an additive and stationary component, thereby paving the way to the application of general-purpose image denoising methods to SAR intensity images. Multi-channel SAR images such as obtained in interferometric (InSAR) or polarimetric (PolSAR) configurations are much more challenging. This paper describes MuLoG, a generic approach for mapping a multi-channel SAR image into real-valued images with an additive speckle component that has a variance approximately constant. With this approach, general-purpose image denoising algorithms can be readily applied to restore InSAR or PolSAR data. In particular, we show how recent denoising methods based on deep convolutional neural networks lead to state-of-the art results when embedded with MuLoG framework.

Starting from a stack of co-registered SAR images in interferometric configuration, SAR tomography performs a reconstruction of the reflectivity of scatterers in 3-D. Scatterers seen within the same resolution cell in each SAR image can be separated by jointly unmixing the SAR complex amplitude observed throughout the stack. In urban areas, Compress Sensing (CS) approaches have been applied to achieve super-resolution in the estimation of the position of the scatterers. However, even if all the local information coming from a stack at a given pixel is used, the structural information that is inherent to the image is not directly used to improve the rendering of the scene. This paper addresses the problem of adding structural constraints to sparse tomographic reconstructions of urban areas. We derive an algorithm allowing the inversion of tomographic data under structural constraints and illustrate its performances on a stack of Spotlight TerraSAR-X images.

In this paper, a generic method is proposed to reduce speckle in multi-temporal stacks of SAR images. The method is based on the computation of a “super-image”, with a large number of looks, by temporal averaging. Then, ratio images are formed by dividing each image of the multi-temporal stack by the “super-image”. In the absence of changes of the radiometry, the temporal fluctuations of the intensity at a given spatial location are due to the speckle phenomenon. In areas affected by temporal changes, fluctuations cannot be ascribed to speckle only but also to radiometric changes. The overall effect of the division by the “super-image” is the spatial stationarity improvement: ratio images are much more homogeneous than the original images. Therefore, filtering these ratio images with a speckle-reduction method is more effective, in terms of speckle suppression, than filtering the original multitemporal stack. After denoising of the ratio image, the despeckled multi-temporal stack is obtained by multiplication with the “super-image”. Results are presented and analyzed both on synthetic and real SAR data and show the interest of the proposed approach.

Reconstruction of elevation maps from a collection of synthetic aperture radar (SAR) images obtained in interferometric configuration is a challenging task. Reconstruction methods must overcome two difficulties: the strong interferometric noise that contaminates the data and the 2π phase ambiguities. Interferometric noise requires some form of smoothing among pixels of identical height. Phase ambiguities can be solved, up to a point, by combining linkage to the neighbors and a global optimization strategy to prevent from being trapped in local minima. This paper introduces a reconstruction method, PARISAR, that achieves both a resolution-preserving denoising and a robust phase unwrapping (PhU) by combining nonlocal denoising methods based on patch similarities and total-variation regularization. The optimization algorithm, based on graph cuts, identifies the global optimum. Combining patch-based speckle reduction methods and regularization-based PhU requires solving several issues: 1) computational complexity, the inclusion of nonlocal neighborhoods strongly increasing the number of terms involved during the regularization, and 2) adaptation to varying neighborhoods, patch comparison leading to large neighborhoods in homogeneous regions and much sparser neighborhoods in some geometrical structures. PARISAR solves both issues. We compare PARISAR with other reconstruction methods both on numerical simulations and satellite images and show a qualitative and quantitative improvement over state-of-the-art reconstruction methods for multibaseline SAR interferometry.

Classification of SAR images is a challenging task as the radiometric properties of a class may not be constant throughout the image. The assumption made in most classification algorithms that a class can be modeled by constant parameters is then not valid. In this paper, we propose a classification algorithm based on two Markov random fields that accounts for local and global variations of the parameters inside the image and produces a regularized classification. This algorithm is applied on airborne TropiSAR and simulated SWOT HR data. Both quantitative and visual results are provided, demonstrating the effectiveness of the proposed method.

Starting from a stack of co-registered SAR images in interferometric configuration, SAR tomography performs a reconstruction of the reflectivity of scatterers in 3-D. Several scatterers observed within the same resolution cell of each SAR image can be separated by jointly unmixing the SAR complex amplitude observed throughout the stack. To achieve a reliable tomographic reconstruction, it is necessary to estimate locally the SAR covariance matrix by performing some spatial averaging. This necessary averaging step introduces some resolution loss and can bias the tomographic reconstruction by mistakenly including the response of scatterers located within the averaging area but outside the resolution cell of interest. This paper addresses the problem of identifying pixels corresponding to similar tomographic content, i.e., pixels that can be safely averaged prior to tomographic reconstruction. We derive a similarity criterion adapted to SAR tomography and compare its performance with existing criteria on a stack of Spotlight TerraSAR-X images.

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) imaging. Since most current and planned SAR imaging satellites operate in polarimetric, interferometric, or tomographic modes, SAR images are multi-channel and speckle reduction techniques must jointly process all channels to recover polarimetric and interferometric information. The distinctive nature of SAR signal (complex-valued, corrupted by multiplicative fluctuations) calls for the development of specialized methods for speckle reduction. Image denoising is a very active topic in image processing with a wide variety of approaches and many denoising algorithms available, almost always designed for additive Gaussian noise suppression. This paper proposes a general scheme, called MuLoG (MUlti-channel LOgarithm with Gaussian denoising), to include such Gaussian denoisers within a multi-channel SAR speckle reduction technique. A new family of speckle reduction algorithms can thus be obtained, benefiting from the ongoing progress in Gaussian denoising, and offering several speckle reduction results often displaying method-specific artifacts that can be dismissed by comparison between results.

This paper presents a method for strong scatterers change detection in synthetic aperture radar (SAR) images based on a decomposition for multi-temporal series. The formulated decomposition model jointly estimates the background of the series and the scatterers. The decomposition model retrieves possible changes in scatterers and the date at which they occurred. An exact optimization method of the model is presented and applied to a TerraSAR-X time series.

Several problems in signal processing and machine learning can be casted as optimization problems. In many cases, they are of large-scale, nonlinear, have constraints, and may be nonsmooth in the unknown parameters. There exists plethora of fast algorithms for smooth convex optimization, but these algorithms are not readily applicable to nonsmooth problems, which has led to a considerable amount of research in this direction. In this paper, we propose a general algorithm for nonsmooth bound-constrained convex optimization problems. Our algorithm is instance of the so-called augmented Lagrangian, for which theoretical convergence is well established for convex problems. The proposed algorithm is a blend of superlinearly convergent limited memory quasi-Newton method, and proximal projection operator. The initial promising numerical results for total-variation based image deblurring show that they are as fast as the best existing algorithms in the same class, but with fewer and less sensitive tuning parameters, which makes a huge difference in practice.

In this paper we propose a new approach for height retrieval using multi-channel SAR interferometry. It combines patch-based estimation and total variation regularization to provide a regularized height estimate. The non-local likelihood term adaptation relies on NL-SAR method, and the global optimization is realized through graph-cut minimization. The method is evaluated both with synthetic and real experiments.

Due to their coherent nature, SAR (Synthetic Aperture Radar) images are very different from optical satellite images and more difficult to interpret, especially because of speckle noise. Given the increasing amount of available SAR data, efficient image processing techniques are needed to ease the analysis. Classifying this type of images, i.e., selecting an adequate label for each pixel, is a challenging task. This paper describes a supervised classification method based on local features derived from a Gaussian mixture model (GMM) of the distribution of patches. First classification results are encouraging and suggest an interesting potential of the GMM model for SAR imaging.

Speckle noise is an inherent problem in coherent imaging systems such as synthetic aperture radar. It creates strong intensity fluctuations and hampers the analysis of images and the estimation of local radiometric, polarimetric, or interferometric properties. Synthetic aperture radar (SAR) processing chains thus often include a multilooking (i.e., averaging) filter for speckle reduction, at the expense of a strong resolution loss. Preservation of point-like and fine structures and textures requires to adapt locally the estimation. Nonlocal (NL)-means successfully adapt smoothing by deriving data-driven weights from the similarity between small image patches. The generalization of nonlocal approaches offers a flexible framework for resolution-preserving speckle reduction. We describe a general method, i.e., NL-SAR, that builds extended nonlocal neighborhoods for denoising amplitude, polarimetric, and/or interferometric SAR images. These neighborhoods are defined on the basis of pixel similarity as evaluated by multichannel comparison of patches. Several nonlocal estimations are performed, and the best one is locally selected to form a single restored image with good preservation of radar structures and discontinuities. The proposed method is fully automatic and handles single and multilook images, with or without interferometric or polarimetric channels. Efficient speckle reduction with very good resolution preservation is demonstrated both on numerical experiments using simulated data, airborne, and spaceborne radar images. The source code of a parallel implementation of NL-SAR is released with this paper.

Tomographic iterative reconstruction methods need a very thorough modeling of data. This point becomes critical when the number of available projections is limited. At the core of this issue is the projector design, i.e., the numerical model relating the representation of the object of interest to the projections on the detector. Voxel driven and ray driven projection models are widely used for their short execution time in spite of their coarse approximations. Distance driven model has an improved accuracy but makes strong approximations to project voxel basis functions. Cubic voxel basis functions are anisotropic, accurately modeling their projection is, therefore, computationally expensive. Both smoother and more isotropic basis functions better represent the continuous functions and provide simpler projectors. These considerations have led to the development of spherically symmetric volume elements, called blobs. Set apart their isotropy, blobs are often considered too computationally expensive in practice. In this paper, we consider using separable B-splines as basis functions to represent the object, and we propose to approximate the projection of these basis functions by a 2D separable model. When the degree of the B-splines increases, their isotropy improves and projections can be computed regardless of their orientation. The degree and the sampling of the B-splines can be chosen according to a tradeoff between approximation quality and computational complexity. We quantitatively measure the good accuracy of our model and compare it with other projectors, such as the distance-driven and the model proposed by Long et al. From the numerical experiments, we demonstrate that our projector with an improved accuracy better preserves the quality of the reconstruction as the number of projections decreases. Our projector with cubic B-splines requires about twice as many operations as a model based on voxel basis functions. Higher accuracy projectors can be used to improve the resolution of the existing systems, or to reduce the number of projections required to reach a given resolution, potentially reducing the dose absorbed by the patient.

Patches have proven to be very effective features to model natural images and to design image restoration methods. Given the huge diversity of patches found in images, modeling the distribution of patches is a difficult task. Rather than attempting to accurately model all patches of the image, we advocate that it is sufficient that all pixels of the image belong to at least one well-explained patch. An image is thus described as a tiling of patches that have large prior probability. In contrast to most patch-based approaches, we do not process the image in patch space, and consider instead that patches should match well everywhere where they overlap. In-order to apply this modeling to the restoration of SAR images, we define a suitable data-fitting term to account for the statistical distribution of speckle. Restoration results are competitive with state-of-the art SAR despeckling methods.

Adding invariance properties to a dictionary-based model is a convenient way to reach a high representation capacity while maintaining a compact structure. Compact dictionaries of patches are desirable because they ease semantic interpretation of their elements (atoms) and offer robust decompositions even under strong speckle fluctuations. This paper describes how patches of a dictionary can be matched to a speckled image by accounting for unknown shifts and affine radio-metric changes. This procedure is used to build dictionaries of patches specific to SAR images. The dictionaries can then be used for denoising or classification purposes.

Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. A fundamental difficulty when comparing two patches from “real” data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared intensity differences. When the noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature. We review seven of those criteria taken from the fields of image processing, detection theory and machine learning. We discuss their theoretical grounding and provide a numerical comparison of their performance under Gamma and Poisson noises.

Image deblurring is essential to high resolution imaging and is therefore widely used in astronomy, microscopy or computational photography. While shift-invariant blur is modeled by convolution and leads to fast FFT-based algorithms, shift-variant blurring requires models both accurate and fast. When the point spread function (PSF) varies smoothly across the field, these two opposite objectives can be reached by interpolating from a grid of PSF samples. Several models for smoothly varying PSF co-exist in the literature. We advocate that one of them is both physically-grounded and fast. Moreover, we show that the approximation can be largely improved by tuning the PSF samples and interpolation weights with respect to a given continuous model. This improvement comes without increasing the computational cost of the blurring operator. We illustrate the developed blurring model on a deconvo-lution application in astronomy. Regularized reconstruction with our model leads to large improvements over existing results.

This paper analyzes the effects of speckle filtering on polarimetric SAR decomposition and classification. We compared the results of the refined Lee, ID AN and Non-Local Polarimetric filters, and discussed their influence on the Cloude-Pottier decomposition and the Wishart H/α classification. ALOS/PALSAR and RadarSat-2 polarimetric SAR data are used for illustration.

Interferometric synthetic aperture radar (SAR) data provide reflectivity, interferometric phase, and coherence images, which are paramount to scene interpretation or low-level processing tasks such as segmentation and 3-D reconstruction. These images are estimated in practice from a Hermitian product on local windows. These windows lead to biases and resolution losses due to the local heterogeneity caused by edges and textures. This paper proposes a nonlocal approach for the joint estimation of the reflectivity, the interferometric phase, and the coherence images from an interferometric pair of coregistered single-look complex (SLC) SAR images. Nonlocal techniques are known to efficiently reduce noise while preserving structures by performing the weighted averaging of similar pixels. Two pixels are considered similar if the surrounding image patches are “resembling.” Patch similarity is usually defined as the Euclidean distance between the vectors of graylevels. In this paper, a statistically grounded patch-similarity criterion suitable to SLC images is derived. A weighted maximum likelihood estimation of the SAR interferogram is then computed with weights derived in a data-driven way. Weights are defined from the intensity and interferometric phase and are iteratively refined based both on the similarity between noisy patches and on the similarity of patches from the previous estimate. The efficiency of this new interferogram construction technique is illustrated both qualitatively and quantitatively on synthetic and true data.

An extension of the non local (NL) means is proposed for images damaged by Poisson noise. The proposed method is guided by the noisy image and a pre-filtered image and is adapted to the statistics of Poisson noise. The influence of both images can be tuned using two filtering parameters. We propose an automatic setting to select these parameters based on the minimization of the estimated risk (mean square error). This selection uses an estimator of the MSE for NL means with Poisson noise and Newton's method to find the optimal parameters in few iterations.

Penalized maximum likelihood denoising approaches seek a solution that fulfills a compromise between data fidelity and agreement with a prior model. Penalization terms are generally chosen to enforce smoothness of the solution and to reject noise. The design of a proper penalization term is a difficult task as it has to capture image variability. Image decomposition into two components of different nature, each given a different penalty, is a way to enrich the modeling. We consider the decomposition of an image into a component with bounded variations and a sparse component. The corresponding penalization is the sum of the total variation of the first component and the L0 pseudo-norm of the second component. The minimization problem is highly non-convex, but can still be globally minimized by a minimum s-t-cut computation on a graph. The decomposition model is applied to synthetic aperture radar image denoising.

Synthetic aperture radar (SAR) images provide scattering information which can be used under any weather conditions for glacier monitoring. Our purpose is to estimate a displacement field characterizing at each position the local speeds and orientations of the glacier displacement. Recent proposed methods build a vector field by tracking patches between two SAR images co-registered on static areas and sensed at different times. The tracking is performed either by evaluating the correlations or the similarities from one acquisition to the other. We propose to estimate locally the displacement vectors by using either the maximum correlation or a maximum likelihood estimator. This local estimation is then refined to provide a sub-pixelic result. The efficiency of both methods are compared.

Recently, non-local approaches have proved very powerful for image denoising. Unlike local filters, the non-local (NL) means introduced in decrease the noise while preserving well the resolution. In the proposed paper, we suggest the use of a non-local approach to estimate single-look SAR reflectivity images or to construct SAR interferograms. SAR interferogram construction refers to the joint estimation of the reflectivity, phase difference and coherence image from a pair of two co-registered single-look complex SAR images. The weighted-maximum likelihood is introduced as a generalization of the weighted average performed in the NL means. We propose to set the weights according to the probability of similarity which provides an extension of the Euclidean distance used in the NL means. Experiments and results are presented to show the efficiency of the proposed approach.

During the past few years, the non-local (NL)means have proved their efficiency for image denoising. This approach assumes there exist enough redundant patterns in images to be used for noise reduction. We suggest that the same assumption can be done for polarimetric synthetic aperture radar (PolSAR) images. In its original version, the NLmeans deal with additive white Gaussian noise, but several extensions have been proposed for non-Gaussian noise. This paper applies the methodology proposed in ato PolSAR data. The proposed filter seems to deal well with the statistical properties of speckle noise and themulti-dimensional nature of such data. Results are given on synthetic and L-Band E-SAR data to validate the proposed method.

Interferometric synthetic aperture radar (SAR) images suffer from a strong noise, and their regularization is often a prerequisite for successful use of their information. Independently of the unwrapping problem, interferometric phase denoising is a difficult task due to shadows and discontinuities. In this paper, we propose to jointly filter phase and amplitude data in a Markovian framework. The regularization term is expressed by the minimization of the total variation and may combine different information (phase, amplitude, optical data). First, a fast and approximate optimization algorithm for vectorial data is briefly presented. Then, two applications are described. The first one is a direct application of this algorithm for 3-D reconstruction in urban areas with very high resolution images. The second one is an adaptation of this framework to the fusion of SAR and optical data. Results on aerial SAR images are presented.

Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the nonlocal means (NL means) algorithm introduced by Buades , which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper, a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a data-driven way. These weights can be iteratively refined based on both the similarity between noisy patches and the similarity of patches extracted from the previous estimate. We show that this iterative process noticeably improves the denoising performance, especially in the case of low signal-to-noise ratio images such as synthetic aperture radar (SAR) images. Numerical experiments illustrate that the technique can be successfully applied to the classical case of additive Gaussian noise but also to cases such as multiplicative speckle noise. The proposed denoising technique seems to improve on the state of the art performance in that latter case.

Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automatic interpretation of images a challenging task and noise reduction is often a prerequisite for successful use of classical image processing algorithms. Numerous approaches have been proposed to filter speckle noise. Markov random field (MRF) modelization provides a convenient way to express both data fidelity constraints and desirable properties of the filtered image. In this context, total variation minimization has been extensively used to constrain the oscillations in the regularized image while preserving its edges. Speckle noise follows heavy-tailed distributions, and the MRF formulation leads to a minimization problem involving nonconvex log-likelihood terms. Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically possible, is not achievable on large images required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (namely the alpha -expansion) is too heavy specially when considering joint regularization of several images. We show that a satisfying solution can be reached, in few iterations, by performing a graph-cut-based combinatorial exploration of large trial moves. This algorithm is applied to joint regularization of the amplitude and interferometric phase in urban area SAR images.

This paper investigates the use of a popular regularization model, the total variation minimization (TV), to filter SAR interferometric images (amplitude and phase data). This model is extensively used for its property of preserving edges and is therefore well adapted for urban areas. Using a TV model adapted to multi-dimensional data, we propose to do a joint filtering of phase and amplitude images. Due to the many local minima, the minimization of such a model is hard to perform. A new fast approximate discrete algorithm is presented. The filtering is applied in the framework of 3D reconstruction. Results on real images are presented.

This paper investigates the joint use of interferometric SAR and optical data for 3D reconstruction. A framework for phase filtering constrained by the discontinuities of the optical image is presented. First, both the amplitude and the interferometric phase are projected in a 3D coordinate system. The problem is then expressed as the regularization of the amplitude and phase images with the introduction as prior knowledge of the edges detected on the optical image. We define the regularized elevation in the framework of Markov random fields (MRF) and derive a smoothness prior that both preserves sharp boundaries (based on total variation minimization) and is driven by the structures present in the optical image. We apply a recent graph-cut based algorithm to perform fast regularization of the elevation field. First results on a real pair of optical and InSAR images are presented.