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.

Interferometric SAR parameters estimation is a very important and challenging problem. The InSAR2InSAR method previously proposed is one of the few self-supervised methods that aims to estimate InSAR parameters. This method has proven to outperform state-of-the-art methods on simulated synthetic data. However, it has to be extended on real data. In this letter, we demonstrate that Sentinel-1 images acquired in the Interferometric Wide Swath mode possess the necessary properties to train and apply InSAR2InSAR effectively. In this paper, we demonstrate the ability of InSAR2InSAR to process across-track Sentinel-1 interferometric images with state-of-the-art performances.

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.

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.

Automatic extraction and recognition of roof structures, surfaces and types from remotely sensed data is one of the most notable challenges for installing urban photovoltaics panels which is of great importance for policymakers planning and investing in distributed energy infrastructures and electrification. DL techniques applied on VHR satellite images, allows to overcome the limitations of roofs surveys in providing this mapping at large scales.This paper proposes a DL based approach for mapping the location and identifying the type of roof surfaces starting from VHR images. The originality of this work is the automatization of roof types classification (metal, concrete, wood, etc.) independently from the country style (Africa, Europe, etc.). Indeed, to classify roof types using DL techniques you need a dataset covering all roof types. Data collection and labelling is usually done manually which is very time consuming. The proposed approach constructs a new dataset of roof types adapted for every region of interest using DL features extraction and clustering. It overcomes the appearance of new roof types especially in developing countries.The experimental results show that proposed approach can effectively and accurately detect and recognize roof types and has competitive performance. .

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/.

Time Series of Satellite Imagery (SITS) acquired by recent Earth observation systems represent an important source of information that supports several remote sensing applications related to monitoring the dynamics of the Earth's surface over large areas. A major challenge then is to design new deep learning models that can take into account intelligently the complementarity between temporal and spatial contexts that characterize these data structures. In this work, we propose to use an adapted self-attention convolutional neural network for spatio-temporal graphs classification that exploits both spatial and temporal dimensions. The graphs will be generated from a series of temporal images that are segmented into different regions. Those graphs are then classified using the Self-Attention Deep Graph CNN (DGCNN) model to highlight the temporal evolution of land cover areas through the construction of a spatio-temporal Map.

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.

Understanding the scene around the ego-vehicle is key to assisted and autonomous driving. Nowadays, this is mostly conducted using cameras and laser scanners, despite their reduced performance in adverse weather conditions. Automotive radars are low-cost active sensors that measure properties of surrounding objects, including their relative speed, and have the key advantage of not being impacted by rain, snow or fog. However, they are seldom used for scene understanding due to the size and complexity of radar raw data and the lack of annotated datasets. Fortunately, recent open-sourced datasets have opened up research on classification, object detection and semantic segmentation with raw radar signals using end-to-end trainable models. In this work, we propose several novel architectures, and their associated losses, which analyse multiple "views" of the range-angle-Doppler radar tensor to segment it semantically. Experiments conducted on the recent CARRADA dataset demonstrate that our best model outperforms alternative models, derived either from the semantic segmentation of natural images or from radar scene understanding, while requiring significantly fewer parameters. Both our code and trained models are available at https://github.com/valeoai/MVRSS.

Recently classical deep learning approaches are commonly used to perform spatial and temporal classification especially for Very High Resolution (VHR) images. They learn from existing low resolution or undersized datasets because of the availability and prices of VHR remote sensing images. Thus, they have witnessed a conspicuous success because it is quite challenging to classify high-dimensional multispectral time series data with few labeled samples. It is also difficult to simulate high quality samples having the same features as the real ones. It goes without saying that the introduction of GANs (Generative Adversarial Network) models as an unsupervised learning method, has allowed the extraction of accurate representations of the data via latent codes and backpropagation techniques. However, it is difficult to acquire high-quality samples with unwanted noises and uncontrolled divergences. To generate high-quality multispectral time series samples, a Self-Attention Generative Adversarial Network (SAGAN) is proposed in this work. SAGAN allows attention-driven, long-range dependency modeling for VHR Multispectral time series image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations which improves training dynamics. The proposed SAGAN performs better than traditional GANs, boosting the best inception score. The main contribution of this work is the use of one of the new generation of learning techniques, SAGAN, for Times Series VHR Multispectral Image Generation. SAGAN has been recently used only for single image generation.

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) and optical satellite image registration is a field that developed in the last decades and gave rise to a great number of approaches. The registration process is composed of several steps: feature definition, feature comparison and optimization of a geometric transformation between the images. Feature definition can be done using simple traditional filtering or more complex deep learning (DL) methods. In this paper, two traditional approaches and a DL approach are compared. One can then wonder if the complexity of DL is worth to address the registration task. The aim of this paper is to quantitatively compare approaches rooted in distinct methodological areas on two common datasets with different resolutions. The comparison suggests that, although more complex, the DL approach is more precise than traditional methods.

High quality perception is essential for autonomous driving (AD) systems. To reach the accuracy and robustness thatare required by such systems, several types of sensors must be combined. Currently, mostly cameras and laser scanners (lidar) are deployed to build a representation of the world around the vehicle. While radar sensors have been used fora long time in the automotive industry, they are still under-used for AD despite their appealing characteristics (notably, their ability to measure the relative speed of obstacles and to operate even in adverse weather conditions). To alarge extent, this situation is due to the relative lack of automotive datasets with real radar signals that are both raw and annotated. In this work, we introduce CARRADA, a dataset of synchronized camera and radar recordings with range-angle-Doppler annotations. We also present a semi-automatic annotation approach, which was used to annotate the dataset, and a radar semantic segmentation baseline, which we evaluate on several metrics. Both our code and dataset are available online.

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.

Very High Resolution (VHR) multispectral Satellite Image Time Series (SITS) enables the production of temporal land cover maps, thanks to high spatial, temporal and spectral resolution of modern earth observation programs. Besides, statistical learning methods applied to SITS monitoring and analysis have created relatively efficient semi-automatic classification techniques. It would therefore be natural to think that the use of deep learning methods on SITS would lead to advances comparable to those known in the field of computer vision. However, when applied to concrete cases, the results are not as convincing. This paper proposes a comparison between a SOTAG (Spatial-Object Temporal Adjacency Graphs) SVM based spatio-temporal classification approach and the Recurrent Neuronal Network (RNN), LSTM (Long Short-Term Memory) model which is trained by historical SITS. The trained LSTM networks are then used to predict new time series data. Both methods perform a spatio-temporal map indicating the temporal profiles of cartographic regions. The proposed approaches will be applied on real and simulated SITS data. We will demonstrate that both results are comparable despite computational times and algorithms complexity.

The Rician distribution is widely used in SAR imagery to model the backscattering of a strong target inside a resolution cell. Nevertheless, the computation of the parameters of the Rice distribution remains a difficult task. In this letter, a new parameterization to model the Rice distribution is introduced. Thanks to the introduction of a new variable defined by the ratio of the target contribution to the speckle, the relationship between the coefficient of variation and this new parameter can be derived. An efficient numerical method is proposed to evaluate it from the coefficient of variation and a discussion on the variance of this estimator is led. A comparison with other methods of estimation showed that the proposed approach is a good compromise between the variance of the estimate and the computation time. At last, a link between the permanent scatterers and the Rice distributed targets is proposed through this new parameterization.

With the advent of very high spatial, spectral, and temporal resolution satellites, Satellite Image Time Series (SITS) analysis and interpretation become even more challenging than before. Besides, several conventional techniques for controlling and monitoring anarchic urban expansion have been initiated but they remain not sufficient to overcome this issue.This paper proposes an automatic method of detection and monitoring of anarchic urban expansions starting from multi-sources and multi-temporal data (VHR satellite images and geographic information data). First, the illegal urban areas are extracted using an original SVM based technique integrating expert knowledge and auxiliary data by means of ontology construction. This leads to the formalization of the expert semantic information and the urban construction rules (often in sentences form) and their confrontation with the classification results.Secondly, the SITS classified images are modeled using Spatial-Object Temporal Adjacency Graphs (SOTAG) constructed for each region of the first image. These graphs are then classified using a Marginalized Graph Kernel (MGK) SVM based classification in order to extract regions with similar temporal evolution. We are focusing mainly on monitoring legal and illegal urban expansions. The resulted spatio-temporal map describes urban areas types and their changes over time.

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.

In remote sensing, temporal sequence of images called Satellite Image Time Series (SITS) covering the same scene allows land cover observation, understanding, analysis and monitoring. Besides, several conventional techniques for controlling and monitoring anarchic urban expansion have been initiated but they remain not sufficient to overcome this issue. This paper proposes an automatic method of detection and monitoring of anarchic urban expansions starting from multi-sources and multi-temporal data (VHR satellite images and geographic information data). First, the illegal urban areas are extracted using an original SVM based technique integrating expert knowledge and auxiliary data by means of ontology construction. This leads to the formalization of the expert semantic information and the urban construction rules (often in sentences form) and their confrontation with the classification results. Secondly, a spatio-temporal regions' similarity framework is proposed using a novel matrix based on Multi-dimensional histograms Earth Mover's Distance (EMD). To this end, an explicit definition of a Spatio-Temporal Region (STR) is given in order to build its characteristic matrix called Multi-Temporal Region Matrix (MTRM). Afterwards, using this matrix, a Cross-STR Similarity Matrix (CSTRSM) is computed between STR of in order to reveal regions with similar temporal fingerprint. The resulted spatio-temporal map describes urban areas types and their temporal changes.

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.

International audience

Change detection is a key problem for many remote sensing applications. In this paper, we present a novel unsupervised method for change detection between two high-resolution remote sensing images possibly acquired by two different sensors. This method is based on keypoints matching, evaluation, and grouping, and does not require any image co-registration. It consists of two main steps. First, global and local mapping functions are estimated through keypoints extraction and matching. Second, based on these mappings, keypoint matchings are used to detect changes and then grouped to extract regions of changes. Both steps are defined through an a contrario framework, simplifying the parameter setting and providing a robust pipeline. The proposed approach is evaluated on synthetic and real data from different optic sensors with different resolutions, incidence angles, and illumination conditions.

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.

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.

In this paper we propose to adapt LSD [1] (a state-of-the-art line segment detector for optical images) to SAR images. The first modification is replacing the gradient computation with an exponentially weighted ratio-based method which has a constant false alarm rate for SAR images. Next, we observe that the strong noise removal necessary for processing SAR images strongly impairs the independent hypothesis of the a contrario model used by LSD. A first order Markov chain is used to take the spatial dependencies into consideration. Experiments show that the proposed method has good performances and the number of false detections is well controlled.

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.

In this paper, we summarize challenges, proposed solutions and recent trends in the field of SAR-optical remote sensing data fusion. Although being a pre-processing step before the actual fusion-by-estimation, it is shown that matching and coregistration is one of the core challenges in that regard, which is mainly due to the strongly different geometric and radiometric properties of the two observation types. We then review some of the published fusion methods and discuss the future trends of this topic.

The aim of this work is to present a resampling scheme for SAR images that preserves spatial resolution and produces statistically accurate images at the same time. Indeed, SAR images are, for reasons due to their acquisition process, well sampled signals according to the Shannon sampling theory. In the presence of strong responses, that we will refer to as targets, a sinc-like function centered at the target is smeared over the entire image and is particularly visible in the range of tens of pixels surrounding the target. To mitigate this phenomenon, the usual solution is to apply an apodization window in the Fourier domain so as to change the cardinal sine impulse response into a much rapidly decaying one. This approach has two major drawbacks. It reduces the resolution of the image and introduces inaccurate statistical dependency between pixels. We propose to resample the image in an adaptive and robust way so that the target smear is canceled and the new sampled image is completely faithful to the underlying signal.

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.

The objective of the Surface Water and Ocean Topography (SWOT) mission is to regularly monitor the height of the earth's water surfaces. One of the challenges toward obtaining global measurements of these surfaces is to detect small water areas. In this article we introduce a method for the detection of thin water surfaces, such as rivers, in SWOT images. It combines a low-level step (segment detection) with a high-level regularization of these features. The method is then tested on a simulated SWOT image.

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.

Temporal sequences of images called Satellite Image Time Series (SITS) allow land cover monitoring and classification by affording a large amount of images. Many approaches attempt to exploit this multi-temporal data in order to extract relevant information such as classification-based techniques. In this paper we compare low and high levels classification-based approaches that aim to reveal the SITS pixels or regions spatio-temporal evolutions through a temporal map. The first approach is a graph region-based classification approach that proved its performances for Very High Resolution (VHR) optical SITS analysis. The second one is a change detection pixel-based approach that has been successfully applied for SAR SITS multi-temporal classification. The experimental results have been conducted on both synthesized and real data in order to compare those approaches and conclude about their accuracies.

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.

In this paper, we address land cover regions monitoring issue by introducing prior knowledge about the studied scene. Actually, remote sensing data growing volumes lead to increase the complexity of direct images interpretation. So, we attempt to overcome this problem by formalizing expert knowledge. The proposed method extends an expert knowledge formalism and temporal evolution graph representation to handle sub-graphs similarity. For this purpose, a user interaction through the proposition of a time window and concepts evolution is introduced. Hence, the proposed approach extracts the most similar temporal evolution to user request over the generated SITS subgraphs. Experiments are performed on synthesized and real STIS and compared to previously presented approaches for STIS analysis.

In this paper we investigate the capacity of the Generalized Gamma distribution to mimick (or imitate) thanks to its three parameters other useful SAR distributions. We first compare it with the Fisher distribution when mimicking a K distribution of reference, thanks to the log-cumulant approach and through a Kullback-Leibler divergence. We then study how the Generalized Gamma distribution can imitate a Log-Normal distribution as asymtotic limit.

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.

In this paper, a method to extract a simplified 3D map of dense urban areas is proposed. It relies on both optical and interferometric SAR images. Firstly, an over-detection of the buildings is done over the optical image using morphological operators. They are modeled as rectangle with a height that is retrieved using the interferometric phase. Then, the problem is modeled as a Markov Random Field and optimized using many different features retrieved in both optical and SAR data.

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.

The scale-invariant feature transform (SIFT) algorithm and its many variants are widely used in computer vision and in remote sensing to match features between images or to localize and recognize objects. However, mostly because of speckle noise, it does not perform well on synthetic aperture radar (SAR) images. In this paper, we introduce a SIFT-like algorithm specifically dedicated to SAR imaging, which is named SAR-SIFT. The algorithm includes both the detection of keypoints and the computation of local descriptors. A new gradient definition, yielding an orientation and a magnitude that are robust to speckle noise, is first introduced. It is then used to adapt several steps of the SIFT algorithm to SAR images. We study the improvement brought by this new algorithm, as compared with existing approaches. We present an application of SAR-SIFT to the registration of SAR images in different configurations, particularly with different incidence angles.

Synthetic aperture radar (SAR) has become an important technique for generating high-resolution images of the ground because of its all-weather capabilities. SAR imaging of stationary scenes is nowadays well mastered. Moving targets induce a delocalization and a defocusing effect in the azimuth direction in a SAR image. This latter effect can be used to detect moving targets, to image them, and to estimate their azimuthal velocity, but the main limitation is the impossibility to estimate the full target velocity vector, because of the Doppler shift dependence on azimuthal position and radial velocity. In this paper, we analyze the performances of a method that reconstructs the real target trajectory given the apparent positions of the moving target measured on SAR images acquired along a circular trajectory. We first outline the steps of this trajectory reconstruction methodology, then we perform a mathematical analysis of this methodology, and finally, we present some results on real data, around two French cities.

In this paper, an extension of the framework proposed by Deledalle et al. [1] for Non Local Means (NLM) method is proposed. This extension is a general adaptive method to denoise images containing multiple noises. It takes into account a segmentation stage that indicates the noise type of a given pixel in order to select the similarity measure and suitable parameters to perform the denoising task, considering a certain patch on the image. For instance, it has been experimentally observed that fetal 3D ultrasound images are corrupted by different types of noise, depending on the tissue. Finally, the proposed method is applied to denoise these images, showing very good results.

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.

In disaster situations, remote sensing images are very useful to quickly assess damages. However, the choice of available images for the studied area is frequently limited. It is often needed to compare images acquired by different sensors and with different acquisition conditions. We propose a new feature-based approach to detect changes between a pair of either optical or radar images. This approach is based on the SIFT algorithm and an a contrario approach. It can deal with multi-resolutions, multi-sensors and multi-incidence angles situations, and it offers promising results.

This paper presents a change detection and classification method of Synthetic Aperture Radar (SAR) multi-temporal images. The change criterion based on a generalized likelihood ratio test is an extension of the likelihood ratio test, in which both the noisy data and the multi-temporal denoised data are used. The changes are detected by a thresholding and then classified into step, impulse and cycle changes according to their temporal behaviors. The results show the effective performance of the proposed method.

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.

This paper presents a denoising approach for multitemporal synthetic aperture radar (SAR) images based on the concept of nonlocal means (NLM). It exploits the information redundancy existing in multitemporal images by a two-step strategy. The first step realizes a nonlocal weighted estimation driven by the redundancy in time, whereas the second step makes use of the nonlocal estimation in space. Using patch similarity miss-registration estimation, we also adapted this approach to the case of unregistered SAR images. The experiments illustrate the efficiency of the proposed method to denoise multitemporal images while preserving new information.

In this paper, we present an image processing chain that can interpret high resolution synthetic aperture radar (SAR) imagery for building layover characterization and extraction in urban areas. It is composed of three main parts - generation of hint areas, generation of superpixels, and optimized cut of layovers via superpixel merging. The proposed framework is complete, and flexibly integrates necessary information, both area and boundary, for building layover extraction; the experimental results show that its performance is promising.

This paper presents a change detection method between two Synthetic Aperture Radar (SAR) images with similar incidence angles and using a likelihood ratio test (LRT). To address the composite hypothesis problem of the LRT, we propose to replace the noise-free values by their estimated results. Thus, a multi-temporal non local means denoising method proposed in [1] is used in this paper to estimate the noise-free values using both spatial and temporal information. The change detection results show the effective performance of the proposed method compared with the state of the art ones, such as log-ratio operator and generalized likelihood ratio test.

The scale invariant feature transform (SIFT) algorithm, commonly used in computer vision, does not perform well on synthetic aperture radar (SAR) images, in particular because of the strong intensity and the multiplicative nature of the noise. We present an improvement of this algorithm for SAR images. First, a robust yet simple way to compute gradient on radar images is introduced. This step is first used to develop a new keypoints extraction algorithm, based on the Harris criterion. Second, we rely on this gradient definition to adapt the computation of both the main orientation and the geometric descriptor to SAR image specificities. We validate this new algorithm with different experiments and present an application of our new SAR-SIFT algorithm.

Starting with a comparison of state of the art criteria and the influence of filtering, this paper presents an analysis tool for multi-temporal SAR images. It is based on the detection of a step change pattern with a generalized maximum likelihood ratio test. Compared to previous works on similar subject, the proposed approach takes into account a filtering step by a non-local approach and a spatially varying equivalent number of looks. Performance analysis is first done on synthetic data and then results on a real data set are analyzed.

In this paper, we are interested in identifying all areas inside a urban canyon that can be illuminated by a radar in NLOS (non line of sight) configuration. We developed a simple model to identify, according to each canyon configuration, the ground canyon areas illuminated by the radar and also the non illuminated areas. We test the results of our algorithm for a specific canyon. To validate our theoretical results we finally present some measurements that will be performed in the anechoic chamber of ONERA on a scaled urban canyon in the case of far field.

This paper presents the first results obtained with satellite image time series (SITS) acquired by Cosmo-SkyMed (CSK) over the Chamonix Mont-Blanc test-site. A CSK SITS made of 39 images is merged with a TerraSAR-X SITS made of 26 images by using the orbital information and co-registration tools developed in the EFIDIR project. The results are illustrated by the computation of speckle-free images by temporal averaging, by the generation and comparison of topographic interferograms and by the measure of glacier displacement fields by amplitude correlation.

This paper presents a processing chain to combine a CosmoSkyMed (CSK) image and a TerraSAR-X (TSX) image. After registration and calibration steps, processing at different levels is studied: pixel level for the detection of stable features and joint filtering, primitive level for stability analysis and object level (like roads) for joint interpretation.

This paper presents a denoising approach for multi-temporal Synthetic aperture radar (SAR) images based on Non-Local Means (NLM) method. To exploit redundancy existing in multi-temporal images, we develop a new strategy of NLM for multi-temporal data. Instead of directly overspreading the NLM operator from one image to temporal images, a two steps weighted average is proposed in this paper. The first step is a maximum likelihood estimate with binary weights on temporal pixels and the second step is iterative NL means on spatial pixels. Experiments in this paper illustrate that the proposed method can effectively exploit image redundancy and denoise multi-temporal images.

Accurate digital elevation model (DEM) estimation using synthetic aperture radar interferometry still remains a challenging problem in the geographical information science community, particularly in dealing with a high noise rate and atmospheric disturbances. Such task suffers from the lack of efficient and reliable methods to overcome these artifacts. This work provides a method that aims to solve this problem through a Bayesian formulation with the Markovian energy minimization framework. The DEM is generated from a set of multifrequency/multibaseline interferograms using a multichannel phase unwrapping algorithm combined with an estimation method of the atmospheric artifacts. A set of experimental results illustrates the effectiveness and robustness of the proposed approach.

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.

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.

The future spatial SWOT mission will use a new altimetric sensor "KaRIn", which is a Ka-band interfero metric SAR system operating on near-nadir swaths on both sides of the satellite track [1]. The objective is to study the height of the earth's water surfaces, mainly oceans, but also continental water surfaces such as lakes and rivers. This article dedicated to water surface detection methods, presents a multi-scale line extraction approach that has been adapted to the specificities of the KaRIn instrument, and gives some preliminary results obtained on simulated SAR images.

In this paper, we propose a hierarchical Markovian framework to extract the road network with optical and synthetic aperture radar (SAR) data. We propose a generalization of a previous method based on a low-level step (features extraction) and a high-level step (use of contextual information). The main novelties of the proposed approach are the use of more general elements to represent road candidates, which simplifies and generalizes the method, the fusion of different sensors during both lower and higher levels and the introduction of a second MRF in a hierarchical way. The approach is tested and evaluated using TerraSAR-X and Quickbird data.

In this letter, we propose a Markovian approach for interferometric synthetic aperture radar (InSAR) phase reconstruction. Recently, Markovian models based on multichannel InSAR likelihood statistics and total variation prior have been proposed to reconstruct the noisy and wrapped phase. Efficient discrete optimization algorithms based on the graph-cut technique are used to efficiently minimize the energy. Our contribution consists in extending these works to cope with continuous label sets providing more precise and accurate reconstructed profiles. The proposed approach also provides a good way to estimate local hyperparameters to adjust the prior model and preserve well discontinuities in profiles. This task is useful when working with real InSAR data where the quantization of the continuous label set leads to a loss of some physical information. The proposed method is compared to other Markovian approaches with discrete multilabel optimization algorithms. Experiments show better quality results both on simulated and real InSAR data.

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.

In this paper, we propose a new complete semi-automatic processing chain able to provide, from a couple of high-resolution optical and synthetic aperture radar (SAR) images, a simple 3-D reconstruction of buildings in urban scenes. A sequence of processing, exploring the complementarities of both optical and SAR data, is developed for building reconstruction. The chain is decomposed into the main following steps: First, potential building footprints are extracted from the monoscopic optical image through global detection followed by a boundary refinement. Then, the optical footprints are projected and registered into SAR data to get a fine superposition between optical and SAR homologous ground features. Finally, the last step, based on the optimization of two SAR criteria, is performed to deal with building validation and height retrieval. Each of these steps is methodologically described and applied on scenes of interest on Quickbird and TerraSAR-X images. A qualification of each reconstructed building by a score of confidence is then proposed. Good results of building detection are obtained, and relevant height estimations are retrieved.

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.

In the literature, several works are led around the radar images especially the detection of the cartographic objects, the 3D reconstruction and the change detection. Concerning this last application, several techniques compete to ensure the best possible result. In this paper, we aim first at developing an automatic detection procedure to compare between similarity measures. Then we propose a change detection technique based on the fusion of two similarity measures. The first one is the Contrast (C) measure [1] and the second one is the Rayleigh Distribution Ratio (RDR) measure [2]. The proposed method has been validated on simulated data and then applied on three radar images.

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.

This paper proposes a new morphological filter for SAR interferograms. It is based on a modified version of alternate sequential filters with reconstruction (MASF), in which the structuring elements are adaptively defined according to the fringe directions. This provides a good fidelity to the fringe information while efficiently removing noise. Another feature of the proposed approach is to apply the filter on the original interferogram and on shifted version, to overcome the wrapping of the phase, and to combine the two results. The proposed filtering technique is then tested on both simulated and real data with different levels of noise. It is also compared to previous techniques according to simplicity and noise reduction.

The aim of this paper is the three dimensional reconstruction of urban areas using Very High Resolution (VHR) images. The proposed innovative approach for the three dimensional reconstruction is based on the joint exploitation of both amplitude and interferometric phase images of a multichannel SAR system. The information provided by the amplitude data is added to the 3D reconstruction chain, considering that in urban areas edges of amplitude image are likely also present in the interferometric phase one and conversely. Differently from other works present in literature, the proposed technique exploits the amplitude image, not only to improve the phase regularization, but also to improve the phase unwrapping step. The results will show the effectiveness of the method.

In this paper, we propose a symmetrized version of a semiautomatic processing chain, able to provide the simple 3D reconstruction of buildings in urban scenes, from high-resolution optical and SAR imagery. The new elaborated chain gives an equivalent part to the optical and SAR components, in order to fully exploit complementary information provided by proper building features in both images. First, the initial processing chain is reminded and completely illustrated on a studied scene on real data. Then, three points of improvements by process symmetrization are discussed: an augmentation of the detection rate in the footprint extraction step, an increase of the reliability attached to the estimated building heights and a joint improvement of the steps of building validation and qualification. Its is shown that the appropriate combination of optical and SAR features, inside some processing steps, could give better results of reconstruction.

We present a new segmentation method for the fully polarimetric synthetic aperture radar (PolSAR) data by coupling the cluster analysis in the tensor space and the Markov random field (MRF) framework. The PolSAR data are usually obtained as a set of 3 × 3 Hermitian positive definite polarimetric covariance matrices, which do not form a Euclidean space. If we regard each matrix as a tensor, the PolSAR data space can be represented as a Riemannian manifold. First, the mean shift algorithm is extended to the manifold to cluster such tensors. Then, under the MRF framework, the data energy term is defined by the memberships of all tensors in all the clusters, and the smoothness energy term is defined according to the cluster overlap rates. These parameters regarding the cluster analysis are computed under the Riemannian framework. The total energy is minimized using a graph-cut-based optimization to achieve the segmentation results. The effectiveness of the proposed method is verified using real fully PolSAR data and synthetic images.

This paper copes with the approximate minimization of Markovian energy with pairwise interactions. We extend previous approaches that rely on graph-cuts and move making techniques. For this purpose, a new move is introduced that permits us to perform better approximate optimizations. Some experiments show that very good local minima are obtained while keeping the memory usage low.

Permanent scatterers (PS) approach allows the identification of radar targets not affected by decorrelation noise then suitable for reliable SAR interferometric measurements. This paper introduces a new technique allowing the selection of stable scatterers based on adaptative SVA (Spatially Variant Apodization) filtering. Indeed, SVA filters identify SAR pixels with strong reflectivity (maximum of the mainlobe) over a long temporal series of interferometric SAR images which is the main feature of Permanent Scatterers pixels. A comparison between PS candidates pixels and selected SVA pixels is discussed in this study.

The increasing quality of satellite images has generated interests in extracting man-made structures in urban areas, such as buildings and roads. A classification adapted to urban areas can help to identify these structures. In this paper, SAR information are used to improve land-cover classification. We proposed a classification process using both radar and optical data, a segmentation and a classification with Support Vector Machines (SVM).

In this paper, we propose to jointly use optical and SAR features issued from satellite images with metric resolution, to deal with the problem of building detection and height retrieval. In a first part, a process described in previous works for building boundary extraction, is briefly exposed and illustrated on a Quickbird urban scene. In a second part, the framework of fusion with SAR data is developed. After the steps of feature projection and registration, a new method for building height estimation is proposed. This one is based on a Likelihood criterion optimization and is built on the scheme "height hypothesis characteristic areas generation energy minimization". Such an approach refers to the adequation between a potential building signature and the real signature, effectively present on the SAR image and defined by characteristic building areas such as layover, shadow, roof and ground/wall echo. This height retrieval process is tested on simulated and real (TerraSAR-X) data.

Markovian approaches have proven to be effective for solving the multichannel phase-unwrapping (PU) problem, particularly when dealing with noisy data and big discontinuities. This letter presents a Markovian approach to solve the PU problem based on a newapriorimodel, the total variation, and graph-cut-based optimization algorithms. The proposed method turns out to be fast, simple, and robust. Moreover, compared with other approaches, the proposed algorithm is able to unwrap and restore the solution at the same time, without any additional filtering. A set of experimental results on both simulated and real data illustrates the effectiveness of our approach.

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.

A new and fast unsupervised technique for segmentation of high-resolution synthetic aperture radar (SAR) images into homogeneous regions is proposed. This technique is based on Fisher probability density functions (pdfs) of the intensity fluctuations and on an image model that consists of a patchwork of homogeneous regions with polygonal boundaries. The segmentation is obtained by minimizing the stochastic complexity of the image. Different strategies for the pdf parameter estimation are analyzed, and a fast and robust technique is proposed. Finally, the relevance of the proposed approach is demonstrated on high-resolution SAR images.

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.

We propose a three step method to extract buildings from the high-resolution PolSAR data, using both the region-based and edge-based information. Firstly, low-level detectors are employed to provide raw region and edge information of the scene. In the second step, improved region-based building detection results are achieved by fusion of label fields, meanwhile the building profile line segments are extracted under a line Markov random field framework. The last step gives the final building footprint estimates: initial rectangle buildings are defined from the building line segments; by optimizing a surface criterion, the final rectangles are retrieved to fit the region-based building detection results. The effectiveness of this method is demonstrated using the real full PolSAR data.

This paper deals with SAR data segmentation in an unsupervised way. The model we propose is a combination of the nonstationary triplet Markov field recently introduced and the Fisher distributions. The first one allows modeling the different stationarities present in a given image. The second one has the advantage that is well adapted to this kind of data. We present an original technique based on Iterative Conditional Estimation method, to estimate the parameters of the model we propose. Application examples on simulated data and real SAR images are presented as well.

With the development of remotely-sensed multisensor satellites like Pleiades Cosmo-Skymed that have the particularity of providing both SAR and optic data, new techniques in image processing are needed. These techniques must take into account the complementarities and differences in nature of these data. A preliminary operation for advanced techniques that use multisensor images such as fusion, classification, etc. is registration. In the case of SAR and optic data, we can do automatic registration if we exactly know the sensor parameters and have a digital terrain model (DTM) or a digital elevation model (DEM) at our disposal. If we do not have an exact knowledge of these parameters, the registration becomes difficult. Another approach to achieve the automatic registration which does not need sensor parameters will rely on comparison measures between both data. In this paper, we present a comparison of several similarity measures between multisensor SAR and optic images used in matching algorithms. An evaluation of these measures for synthetic data based on their distributions is given. Then results on real images are analyzed.

Tropospheric inhomogeneities can form a major error source in differential synthetic aperture radar interferometry measurements, which are used in slow-deformation monitoring. Indeed, variations of atmospheric conditions between two radar acquisitions produce variations in the signal path of two images and, thus, additional fringes on differential interferograms. These effects have a strong influence on interferograms and must be compensated to obtain reliable deformation measurements. This paper presents a methodological approach to reduce at both global and local scales tropospheric contributions directly from differential interferograms. It first requires refined knowledge of the stable scatterers that can only be obtained from the analysis of a large population of multitemporal interferograms. The correction of global-scale atmospheric contribution exploits the correlation between phase and topography. The correction of local artifacts is based on the correlation between interferograms containing one common acquisition. This technique is validated on a database of 81 differential interferograms covering the Gulf of Corinth (Greece) and used to improve the measurements of ground deformation compared to global positioning system measurements

The retrieval of 3-D surface models of the Earth is a major issue of remote sensing. Some nice results have already been obtained at medium resolution with optical and radar imaging sensors. For instance, missions such as the Shuttle Radar Topography Mission (SRTM) or the SPOT HRS have provided accurate digital terrain models. The computation of a digital surface model (DSM) over urban areas is the new challenging issue. Since the recent improvements in radar image resolution, synthetic aperture radar (SAR) interferometry, which had already proved its efficiency at low resolution, has provided an accurate tool for urban 3-D monitoring. However, the complexity of urban areas and high-resolution SAR images prevents the straightforward computation of an accurate DSM. In this paper, an original high-level processing chain is proposed to solve this problem, and some results on real data are discussed. The processing chain includes three main steps, namely: (1) information extraction; (2) fusion; and (3) correction. Our main contribution addresses the merging step, where we aim at retrieving both a classification and a DSM while imposing minimal constraint on the building shapes. The joint derivation of height and class enables the introduction of more contextual information. As a consequence, more flexibility toward scene architecture is possible. First, the initial images (interferogram, amplitude, and coherence images) are converted into higher-level information mapping with different approaches (filtering, object recognition, or global classification). Second, these new images are merged into a Markovian framework to jointly retrieve an improved classification and a height map. Third, DSM and classification are improved by computing layover and shadow from the estimated DSM. Comparison between shadow/layover and classification allows some corrections. This paper mainly addresses the second step, while the two others are briefly explained and referred to already published papers. The results obtained on real images are compared to ground truth and indicate a very good accuracy in spite of limited image resolution. The major limit of DSM computation remains the initial spatial and altimetric resolutions that need to be made more precise

This letter aims at the extraction of roads and road networks from high-resolution synthetic aperture radar data. Classical methods based on line detection do not use all the information available; indeed, in high-resolution data, roads are large enough to be considered as regions and can be characterized also by their statistics. This property can be used in a classification scheme. Therefore, this letter presents a road extraction method which is based on the fusion of classification (statistical information) and line detection (structural information). This fusion is done at the feature level, which helps to improve both the level of likelihood and the number of the extracted roads. The proposed approach is tested with two classification methods and one line extractor. Results on two different datasets are discussed.

A general processing framework for urban road network extraction in high-resolution synthetic aperture radar images is proposed. It is based on novel multiscale detection of street candidates, followed by optimization using a Markov random field description of the road network. The latter step, in the path of recent technical literature, is enriched by the inclusion of a priori knowledge about road junctions and the automatic choice of most of the involved parameters. Advantages over existing and previous extraction and optimization procedures are proved by comparison using data from different sensors and locations

Synthetic aperture radar (SAR) interferometry enables to compute an height map of the scene which is useful for many applications. Yet the complexity of high resolution SAR images and of urban areas prevents from computing an accurate DSM easily and a high level processing chain is thus required. In this article, we propose a Markovian fusion scheme to retrieve jointly the height map and the classification. The original data (amplitude, interferogram and coherence) are first processed in order to get new entries. They represent advanced information on the scene, extracted with different approaches (filtering, object recognition or global classification). These features are then merged in a Markovian framework to recover an improved classification and height map. The method is illustrated on real data.

Three-dimensional reconstruction in urban areas is one of the major issues in remote sensing applications. SAR interferometry (InSAR) has a great potential to provide Digital Surface Models (DSM), but the context of urban areas and high resolution (HR) is difficult (geometrical distortions, surface heterogeneities, speckle, height discontinuities). Previous studies prove both the difficulty and the potential of the method. In this paper, we propose a new fusion method to compute DSM from HR InSAR. The merging approach enables to take into account several kinds of information extracted from the original data in order to retrieve jointly a classification and a DSM. The paper will be focused on the analysis of the results in order to understand the limits and potentials of InSAR over urban areas. Before discussing the results, the method will be briefly described.

This paper deals with the estimation of an elevation model using a pair of synthetic aperture radar (SAR) images and an optical image in semiurban areas. The proposed method is based on a Markovian regularization of an elevation field defined on a region adjacency graph (RAG). This RAG is obtained by oversegmenting the optical image. The support for elevation hypotheses is given by the structural matching of features extracted from both SAR images. The regularization model takes into account discontinuities of buildings thanks to an implicit edge process. Starting from a good initialization, optimization is obtained through an iterated conditional mode algorithm.

SAR and differential SAR interferometry are operational tools for monitoring surface deformation and topographic profile reconstruction. However, they still have limitations due to temporal and geometric decorrelation. These disturbances strongly compromise the accuracy of the results, but reliable measurements can be obtained over a large multitemporal population of interferograms. In this paper we propose a new algorithm for monitoring temporal evolution of ground surface using several interferograms covering ground movements over a long period of time. It is based on a statistical approach with hypothesis test. The objective of multiple interferograms elevation retrieval is to deal with noisy data. Another important advantage of the multiple image processing is that all baselines (small or large) are considered. The method described in this paper can he applied in both SAR and differential SAR interferometry context. We chose to study the SAR interferometry case

Discontinuous objects, such as buildings, produce shadows in SAR images. Shadows are striking features which greatly help in the image understanding. Due to the high density of buildings in urban areas, shadows cover a large part of the image and provide a major hint to build a map of the city. A straightforward use of the shadows is to determine the building height from the shadow dimensions. We propose another approach here which makes use of the shadow to help in detecting the building itself when a high resolution interferogram is available. Starting from an amplitude image with very high definition and the corresponding interferogram, we model the building detection problem as an energy minimization where the interaction between a building and its shadow is taken into account. The method allows to obtain excellent detections especially for high or isolated building, despite the important noise level.

The aim of this paper is to propose a framework for the use of both SAR and optical data in a 3D reconstruction process. The SAR data provide height information either by interferometric or radargrammetric process and the optical data provides building shapes. The method is based on a Markov random field defined on a region adjacency graph. The regions are obtained using a segmentation of the optical image. The graph is then fed by the height information (either interferometric or radargrammetric) computed with the SAR data. The Markovian regularization takes height discontinuities into account thanks to an implicit edge process.

We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for high-resolution SAR images over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian scattering statistics (K, Weibull, Log-normal, Nakagami-Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. As a consequence, we use a mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data. This estimation method is based on the second-kind statistics, which are detailed in the paper. We also prove its accuracy for urban areas at high resolution. The quality of the classification that is obtained by mixing this model and a Markovian segmentation is high and enables us to distinguish between ground, buildings, and vegetation.

The presence of atmospheric contributions in SAR interferograms represents the main limit for the detection of ground deformation movements. This paper presents a methodological approach to reduce at both global and local scales the tropospheric contributions in the interferograms. It first requires the refined knowledge of the permanent scatterers that can only be obtained from the analysis of a large population of interferograms. The correction of global scale atmospheric contribution exploits the correlation between phase and topography and the correction of local artefacts is based on correlation between interferograms containing one common acquisition.

The framework of this study is classification of high resolution SAR images over urban areas. Statistics of these images reflect the presence of strong reflectors scattered all over; therefore histograms have a heavy tail. We propose a new distri- bution model (Fisher distribution) to fit such probability density functions. As its moments are not defined for all parameter values, we use a second kind statistics based estimation (log- moment estimation). The purpose of this article is the validation of both estimation method and distribution model. We first prove that, in this context, log-moment method is more accurate than moment method. We also demonstrate that Fisher functions are the most accurate for man-made structures. Finally these distributions are used in a Markovian classification.

Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert-Peano scan of the image, constitute a fast and robust alternative to hidden Markov random field models for spatial regularization of image analysis problems, even though the latter provide a finer and more intuitive modeling of spatial relationships. We here compare the two approaches and show that they can be combined in a way that conserves their respective advantages. We also describe how the distribution families and parameters of classes with constant or textured radar reflectivity can be determined through generalized mixture estimation. Sample results obtained on real and simulated radar images are presented.

SAR images are classically analyzed with the help of Goodman approach and multiplicative noise. By this way, speckle is modeled by a Gamma law (for intensity images). A new approach based on "second kind statistics", J. M. Nicolas et al., (2000), identifies multiplicative noise as a "Mellin convolution", J. M. Nicolas et al., (1998), yielding oversimple expression when texture is not homogeneous. In this article, we propose to use this new approach for solving the problem of binary additive mixture of Gamma law, J. M. Nicolas (2001), and to apply the results to SAR image processing.

The problem of interferometric SAR image coregistartion is addressed. For classical images, the application of the Symmetric Phase Only Matching Filtering (SPOMF) to the Fourier-Mellin Invariant (FMI) descriptors allows an accurate and efficient registration of translated, rotated and scaled images. This paper discusses an extension of the technique to cover the FMI descriptors of two interferometric SAR images. This method is tested on two pairs of InSAR data in France and Tunisa. The results are compared with those of classical cross-correlation registration techniques.

The aim of this paper is to study the use of cross-correlation for radargrammetric applications. Two other criteria derived from the mean square error analysis are proposed. These three criteria are studied first through their distributions computed using simulated data, and secondly when applied on synthetic and real SAR images. Besides, the influence of the use of logarithm or averaged data is studied.

This paper deals with the automatic extraction of the road network in dense urban areas using a few-meters-resolution synthetic aperture radar (SAR) images. The first part presents the proposed method, which is an adaptation of previous work to the specific case of urban areas. The major modifications are 1) the clique potentials of the Markov random field that extracts the road network are adapted and 2) a multiscale framework is used. Results on shuttle mission and aerial SAR images with different resolutions are presented. The second part is dedicated to road extraction combining two SAR images taken with different flight directions (orthogonal and antiparallel passes), and the obtained improvement is analyzed.

This paper presents a new definition of a spatial entropy mainly based on the Markov random field (MRF) properties. Starting with the study of the entropy proposed by Volden, Giraudon and Berthod (1995) for the Potts model, we establish a specific property of the entropy in this special case, and derive the analytical expressions for a 4-connexity neighborhood. Inspired by the previous property, we propose a new definition mainly based on an heterogeneity measure of the neighborhood. This definition is then used to analyze a SAR (synthetic aperture radar) image and it is shown to be able to discriminate different types of textures.

The authors propose a method aiming to characterize the spatial organization of the main cartographic elements of a synthetic aperture radar (SAR) image and thus giving an almost automatic interpretation of the scene. Their approach is divided into three main steps which build the whole image interpretation gradually. The first step consists of applying low-level detectors taking the speckle statistics into account and extracting some raw information from the scene. The detector responses are then fused in a second step using Dempster-Shafer theory, thus allowing the modeling of the knowledge that there is about operators, including possible ignorance and their limits. A third step gives the final image interpretation using contextual knowledge between the different classes. Results of the whole method applied to different SAR images and to various landscapes are presented.

The authors propose a two-step algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images. The first step is local and is used to extract linear features from the speckle radar image, which are treated as road-segment candidates. The authors present two local line detectors as well as a method for fusing information from these detectors. In the second global step, they identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects. The influence of the parameters on the road detection is studied and results are presented for various real radar images.