SAR-to-optical translator networks are especially used to overcome the lack of optical images under cloudy conditions. Those translations being used for downstream tasks, they require the reconstruction of reliable patterns with respect to the underlying objects. In this paper, we propose a novel training strategy to account for land-cover complexity through a conjoint Translation-Classification Loss (TCL). The proposed loss evaluates the classifiability of translated images with a pre-trained land-cover classifier by assessing the reliability of its predictions and the relevance of its extracted hidden features. This new loss is applied to nine translators from the literature and to a tenth architecture introduced in the paper. Experiments show that applying the TCL not only improves the credibility of structures, patterns and textures but it also allows for better class discrimination and transitions while avoiding unreliable hallucinated artifacts produced by standard losses in adversarial approaches.

Detecting slope instabilities on Synthetic Aperture Radar (SAR) interferograms using deep learning approaches presents several challenges. This detection task suffers from the lack of transparency of deep networks, the complexity of the input data (i.e. complex values, sensitivity to distortions and presence of counterfactuals) and the complexity of the target phenomena (i.e. the variable velocities and the complex underground processes). In this paper, we propose a new framework called ”Explainability Constrained-claSsifier for Pairing the detection and the Localization of moving Areas on INterferograms” (ECSPLAIN), to generate decision, localization and segmentation maps from a single but explainable classifier network. It consists of training a classifier to detect whether an instability is located in the patch or not, and to explain its decision with a Class Activation Map (CAM) that matches the actual location of the instability. Therefore by using a single classifier network, the framework can pair the detection and the localization of moving areas. Four CAMs are investigated for the training of the ECSPLAIN framework. Experiments on the ISSLIDE dataset show that our proposal achieves better explainability than standarda posterioriCAMs with more than 0.20 points of improvements in terms of Dice and IoU scores. It also allows competitive performance with segmentation-only networks with only 0.04 points of difference in terms of Dice and IoU scores. Thus, the proposed method is competitive with the most efficient methods while being lighter, faster, and delivering a decision based on a human-like reasoning process. Finally, the ECSPLAIN framework is applied to enrich the ISSLIDE dataset, discovering more than 470 manually validated slope instabilities over the Alps.

Snow is a vital environmental parameter that holds significance across various disciplines, such as hydrology, meteorology, and natural disaster management. With the increasing accessibility of snow products derived from Synthetic Aperture Radar (SAR) and optical data, like Sentinel-1 wet snow and Sentinel-2 total snow, users have benefited from improved snow mapping and monitoring. However, snow mapping in the mountainous areas remains challenging due to the difficulty of obtaining reliable ground truth data on steep mountain terrain. In this study, we introduce a deep semantic segmentation framework, SACUNet, specifically designed for wet snow detection from SAR image time series in mountainous environments. To address the lack of ground truth, we constructed a high-confidence training and validation database through a rigorous decision-fusion process combining multi-temporal Sentinel-1 wet snow detections with Sentinel-2 total snow maps. We also propose two complementary metrics, the Conditional Agreement Rate (CAR) and the Wet Snow Intersection over Union (WSIoU), to quantify the robustness and consistency of the fusion procedure, therefore ensuring the reliability of training labels in the absence of in-situ data. SACUNet integrates advanced techniques like: (i) Depthwise Separable Convolution, which captures cross-channel dependencies and adapts feature representations, and (ii) Atrous Separable Convolution, which further refines and consolidates the learned features, into the U-Net architecture. The proposed framework has been successfully employed to monitor wet snow in the Mont-Blanc massif, using a time series of 69 Sentinel-1 images acquired from 05 July 2020, to 29 August 2021. SACUNet demonstrates remarkable accuracy in wet snow detection, with an Overall Accuracy of 97%, Precision of 94%, Recall of 97%, Intersection over Union at 92%, and an F1-Score reaching 96%. Validation against meteorological records from four alpine stations confirmed that SACUNet effectively tracks seasonal wet snow dynamics, suppresses false detections during cold periods, and captures realistic high-altitude melt events. Moreover, the model trained in Mont-Blanc generalized successfully to the Vanoise massif, demonstrating its transferability to other alpine regions. Beyond quantitative accuracy, SACUNet enables the spatio-temporal analysis of wet snow evolution, offering insights into its extent, frequency, and seasonal progression across elevation bands. These findings highlight the framework’s potential as an operational tool for large-scale wet snow monitoring in mountainous environments.

SAR-to-optical remote sensing translator neural networks are mostly trained on flat areas, avoiding SAR geometrical distortion issues in steeply sloped areas. Their degraded performance under such topology severely limits the ability to detect disasters such as landslides in cloud covered areas. In this paper, we first propose a new SAR-DEM-optical dataset in mountainous regions to improve the performance of SAR-to-optical image translators under these extreme conditions. Then we upgrade SARDINet (SAR Distorted Image translator Network) model previously developed for urban areas, to take a Digital Elevation Model (DEM) together with the SAR image as input and perform translation in a natural mountain environment. Several fusion strategies are explored to efficiently merge SAR and DEM images: late fusion, early fusion and an intermediate fusion based on balanced separable convolutions. These approaches show improvements in distorted regions compared to the original SARDINet and two standard adversarial networks - Pix2pix and CycleGAN.

This study explores machine learning for classifying X-band Synthetic Aperture Radar (SAR) monovariate time series from four cryospheric zones in the Mont-Blanc massif. We aim to classify ablation zones, accumulation zones, hanging glaciers, and ice aprons using log-cumulants and Dynamic Time Warping Barycentric Averaging. Our approach evaluates distances between time series and estimated reference centroids, employing HH and HV polarimetric channels. We propose an extension to this method by aggregating class membership probabilities from selected polarimetric combinations. Results are compared across polarimetric channels, revealing insights into classification performance.

Due to the high data demand of machine learning algorithms, multiple datasets are emerging in remote sensing. But these datasets are costly and time consuming to annotate especially for change detection or natural phenomena monitoring. In particular, early warning systems on slow-moving disasters are lacking of training datasets as they require both geomorphological and SAR interferometry expertise. In this paper, (i) we propose a novel InSAR dataset for Slow SLIding area DEtection (ISSLIDE) with machine learning algorithms. The latter consists of manually annotated patches of generated interferograms over slow moving areas. (ii) We implement the segmentation of ISSLIDE interferograms with classical deep learning approaches. FCN, DeepLabV3 and U-Net-like architectures are explored to serve as baseline for future works. To the best of our knowledge, this is the first dataset adapted to machine learning and targeting slow sliding area detection.

We propose a new approach for wet snow extent mapping in Synthetic Aperture Radar (SAR) images by using a convolutional neural network (CNN) designed to learn with respect to snowpack outputs from the state-of-the-art snow model Crocus. The CNN was trained to classify the wet snow conditions based on features extracted from the SAR images, using both the VV,VH channel and the ratio between these channels and those of a reference image in summer. One of the key points of this work is the comprehensive comparison we have made between the performance of the CNN method and other advanced statistical methods. We found that the CNN was able to achieve good accuracy in wet snow classification, and giving a complementary vision of the solutions obtained by other machine learning algorithms such as the Random Forest classifier. The results of this study demonstrate the potential of using CNNs and SAR images for wet snow classification and highlight the importance of using physical information model for training machine learning models in snow state identification, a domain where collecting ground truth is intricate due to the complexity of the snowpack moisture measurement systems.

In this paper, two SAR image time series acquired by PAZ and Sentinel-1 satellites in 2020 (29 and 60 images respectively) are used to investigate surface changes of different ice/snow-covered areas in the Mont-Blanc massif. The evolution of the backscatter coefficient and several statistical parameters in both X and C band SAR images is analyzed on ice aprons, on valley glacier accumulation and ablation areas, and on ice-free areas. Dry and wet snow changes are observed and correlated with meteorological data (temperature at 4 different elevations and snow height) acquired by a weather station.

Snow holds a significant importance as a fundamental environmental factor in multiple domains. Obtaining accurate ground truth data for snow mapping in mountainous areas presents a significant challenge. To address this issue, this paper presents a deep semantic learning framework for the segmentation of Sentinel-1 images for wet snow detection in mountainous areas. Firstly, we propose to create a deep leaning database based on snow products derived from Sentinel-1 and Sentinel-2 data. Afterward, we introduce a deep convolutional neural network called ReXcepUnet, which combines the U-Net architecture and the powerful Xception backbone. Finally, the proposed framework has been successfully applied to monitor wet snow in the Mont Blanc massif, yielding high accuracy results. The ReXcepUnet model demonstrates a good performance in wet snow detection, particularly in high-relief regions like the Mont Blanc massif.

Multimodal approaches for Earth Observations suffer from both the lack of interpretability of SAR images and the high sensitivity to meteorological conditions of optical images. Translation methods were implemented to solve them for specific tasks and areas. But these implementations lack of generalizability as they do not include samples with challenging characteristics. Firstly, this paper sums up the main problems that a general SAR to optical image translator should overcome. Then, a SAR Distorted Image to optical translator Network (SARDINet) alternating knowledgeable channel-wise spatial convolutions and cross-channel convolutions is implemented. It aims at solving a problem of major concern in remote sensing: translating layover disturbed SAR images into disturbance-free optical ones. SARDINet is trained through a classical and an adversarial framework and compared to cGAN and cycleGAN from the literature. Experimental results prove that adversarial approaches are more qualitative but worsen quantitative results.

This paper focuses on understanding the temporal behaviour of Ice Aprons (IAs) in the Mont-Blanc massif using high resolution SAR coherence matrix. InSAR coherence is estimated between all possible pairs of TerraSAR-X images acquired in 2009 and 2011 as well as between PAZ images acquired in 2020, both at 11-day interval. The results show that coherence values in summer are higher in 2020 than in 2009 and 2011. Coherence matrices are also computed for different regions of a glacial system. The results are compared with those of IAs to understand the differences in their temporal and physical behaviours. In summer, all IAs show an increase in coherence values, while other glacier regions show very low or no coherence. This information could be useful for automatic classification methods, where IAs could be classified separately as a different class from the other types of glaciers.

Numerous glacier velocity observations, derived from spaceborne imagery, are available online, but it remains difficult to analyze them because they are measured with different temporal baselines, by various sensors. In this study, we propose a novel formulation of the temporal closure to fuse multi-temporal multi-sensor velocity observations without prior information on the displacement behavior and the data uncertainty. We establish a system of linear equations between combinations of displacement observations and fractions of estimated displacements. The proposed approach provides a velocity time-series with a regular and optimal temporal sampling, the latter representing a compromise between the temporal resolution and the signal-to-noise ratio. The proposed approach is first evaluated on synthetic datasets and second on Sentinel-2 and Venμs velocity observations over the Fox glacier in New Zealand. The results show the intra-annual variability of Fox glacier surface velocity with a reduced uncertainty and complete temporal coverage.

Today, more and more velocity observations are available online or on-demand. However, this amount of data is complex to analyze since velocity observations span different temporal baselines. Velocities obtained from a small temporal baseline are close to the derivative of the displacement but are more likely to be contaminated by noise. Velocities obtained from a long temporal baseline approximate the mean velocity between two dates but can be affected by temporal decorrelation. Having short and long temporal baselines provides a data redundancy that needs to be properly considered. In this article, we propose a method that aims to extract short-term velocity time series with regular temporal sampling from all available displacement observations. The proposed method relies on a temporal inversion based on an improved temporal closure of the displacement observation network. Two criteria are proposed to determine the optimal temporal sampling to study short-term variations. To take the unequal data uncertainty into account, the temporal inversion is done by an iterative reweighted least square using a well-established weighting function, without preprocessing. The proposed method results in velocity time series with an optimal temporal sampling, improved temporal coverage, reduced uncertainty, and no redundancy. The studied area is the Kyagar glacier, in the North of the Karakoram range that is characterized by strong velocity variations originated from a glacier surge and additional seasonal variability.

This article proposes a method based on the temporal closure of the displacement measurement's network. The aim is to extract short-term glacier velocities and to use data redundancy to reject outliers and reduce uncertainty. By using all the available displacement measurements, we retrieve a displacement time series between consecutive observation dates by means of an inversion. The proposed inversion method is an Iterative Weighted Least Square (IWLS) with a regularization on the discrete derivative of displacements. We apply our method to a glaciers velocity data-set covering Fox Glacier in the Southern Alps of New Zealand.

This paper focuses on the visibility of glacier surfaces in the Mont Blanc Massif (Western European Alps) on TerraSAR-X/PAZ images and the identification of geometric distortions (GDs) based on the SAR acquisition geometry. Small glaciers which exist in complex topographies like steep slopes are most prone to GDs. We built a visibility map for both ascending/descending orbits by utilizing previously documented algorithms like the R-Index (RI) and the Layover Shadow (LS) simulations, combined with an analysis of the angle between the steepest slope direction (SSD) and line of sight (LOS) vectors. The visibility map allows us to identify glaciers on steeper slopes which should be considered for further analysis using TerraSAR-X/PAZ images.

This brief addresses understandability of modern machine learning networks with respect to the statistical properties of their convolution layers. It proposes a set of tools for categorizing a convolution layer in terms of kernel property (meanlet, differencelet, or distrotlet) or kernel sequence property (frame spectra and intralayer correlation matrix). These tools are expected to be relevant for determining the generalization capabilities of a convolutional neural network. In particular, this brief highlights that the less frequency penalizing network among AlexNet, GoogleNet, RESNET101, and VGG19 is the more relevant one in terms of solutions for low-level ice-sheet feature enhancement.

In this paper, we propose a new method to combine displacement measurements from images with a low signal to noise ratio. The method takes advantage of the temporal redundancy of displacements that can be calculated from different image pairs to combine them in a single relative displacement time series robust to outliers. The method has only two parameters which determines the smoothness of the result. The proposed algorithm has been tested on displacements calculated from ground based stereo images of the Laurichard rock glacier. On the test data, our method outperforms the traditional inversion method by providing relative displacement time series with negligible noise and no outlier.

In this paper, a data-adaptive method, namely, principal modes (PM) method, based on the spatially averaged temporal covariance of a time series of InSAR displacement measurement obtained from consecutive SAR acquisitions is proposed to retrieve the displacement signal for decorrelating targets. On wrapped interferogram time series, the PM method can highlight and restore coherent fringe patterns where they are more or less significantly hindered by decorrelation noise, whereas on unwrapped interferogram time series, the PM method provides a satisfactory separation of the displacement signal from the spatially correlated perturbations. A two-stage application of the PM method to both wrapped and unwrapped interferogram time series can significantly improve the retrieval of the displacement signal. Synthetic simulations are first performed to investigate the impact of the choice of the appropriate number of modes to retain in the empirical orthogonal function decomposition and of the time series size on the performance of the PM method, as well as to highlight the efficiency of the PM method. Then, the PM method is applied to time series of wrapped and unwrapped Sentinel 1 A/B interferograms over the Gorner glacier between October 2016 and April 2017. The main characteristics of the PM method, such as realistic assumptions, ease of implementation, and high efficiency, are highlighted.

In this paper, we present new results of the use of Sentinel-1 data to monitor Alpine glacier displacement by SAR differential interferometry (D-InSAR) in Chamonix-Mont-Blanc Valley. Two time series of Sentinel-1 A/B images acquired from October 2016 to early April 2017 (including 31 ascending and 25 descending acquisitions) are used to form 6-day interferograms and to evaluate their potential for displacement measurements over small fast moving Alpine glaciers. Results show that, even at low latitudes as in the French Alps, fringe patterns can be observed over the glaciers during the cold season with favorable anti-cyclonic meteorological conditions. Different processing steps to derive final displacement fields are presented and discussed and the results are compared with ERS-Tandem results obtained on the same glaciers in winter 1996.

Dynamic systems such as glaciers can be studied using Displacement Field Time Series (DFTS), often derived from Satellite Image Time Series. Even if data mining patterns expressing interesting displacement evolutions can be extracted from DFTS, confidence measures coming along with these series have to be considered to focus on reliable evolutions. This paper introduces a new approach for selecting displacement evolutions that are reliable, informative and complementary. Reported experiments exhibit consistent displacement evolutions of Alpine glaciers and complete the current knowledge of the area.

In this paper, a data-adaptive method, namely Principal Modes (PM) method, based on the spatially averaged temporal covariance of a time series is proposed to extract the displacement signal from a time series of Sentinel 1 A/B inter-ferograms over the Gorner glacier during the period between October 2016 and April 2017. On unwrapped interferogram time series, the PM method provides a satisfactory separation of the displacement signal from the spatially correlated perturbations, while on wrapped interferogram time series, the PM method can highlight fringe patterns where they are more or less significantly hindered by the decorrelation noise.

This paper investigates the problem of glacier flow estimation using Synthetic Aperture Radar (SAR) image data. Our motivation is to exploit a weighted graph model constructed from characteristic points (i.e. keypoints) to measure the displacement vectors located at their positions. In fact, characteristic points are capable of capturing the image's radiometric and contextual information. Then, by encoding their interaction and inter-connection, a graph model is able to characterize both intensity and geometry information from the image content, which is relevant for texture tracking task. In this work, we employ a graph-based similarity measure to track the local texture information around each keypoint in order to figure out its correspondence from the other image and calculate the associated displacement. The proposed approach is tested and evaluated using high resolution TerraSAR-X images acquired from the Argentiere Glacier located in the French Alps. Our preliminary experimental results show the algorithm's capacity to provide a fast and reliable estimation of glacier flows, especially over highly textured and structured regions.

At the end of the 20th century, the development of spatial geodetic techniques (optical & SAR imagery, GPS) has allowed for drastic improvement of the spatial coverage and the resolution of the displacement measurements. The arrival of these techniques has caused an effective revolution by significantly improving our ability to measure the ground movement, as well as their temporal evolutions with great precision over large areas. Spectacular results have been obtained in numerous applications with displacement of various characteristics (in terms of magnitude, duration, spatial distribution): the study of subsidence in urban areas, of the co-seismic, inter-seismic and post-seismic motions, of glacier flows, of volcanic deformation, etc. Nowadays, the displacement maps obtained by remote sensing techniques cover almost the whole world, with a precision within millimetres per year. Therefore, they are considered as the predominant sources for studies of the terrestrial deformation, from which geophysical models of the deformation have been retrieved to further understand the deformation source in depth. To this end, good knowledge of the reliability of the remote sensing data, as well as of the physical models accordingly obtained is crucial for all the researches and applications that use these sources of information. A perspective of significant improvement in the accuracy of the displacement measurement appears with the growing availability of remote sensing data. Methodological development in fusion of displacement measurements and of the integration of a physical model based on the supercomputer facilities seems necessary to reduce the uncertainty and to improve the accuracy of the displacement measurement. In this context, this paper addresses the current status, challenges and perspectives of the remotely sensed displacement measurement fusion.

This paper first provides statistical properties of wavelet operators when the observation model can be seen as the product of a deterministic piecewise regular function (signal) and a stationary random field (noise). This multiplicative observation model is analyzed in two standard frameworks by considering either: 1) a direct wavelet transform of the model; or 2) a log-transform of the model prior to wavelet decomposition. The paper shows that, in Framework 1, wavelet coefficients of the time series are affected by intricate correlation structures which blur signal singularities. Framework 2 is shown to be associated with a multiplicative (or geometric) wavelet transform, and the multiplicative interactions between wavelets and the model highlight both sparsity of signal changes near singularities (dominant coefficients) and decorrelation of speckle wavelet coefficients. This paper then derives that, for time series of synthetic aperture radar data, geometric wavelets represent a more intuitive and relevant framework for the analysis of smooth earth fields observed in the presence of speckle. From this analysis, this paper proposes a fast-and-concise geometric-wavelet-based method for joint change detection and regularization of synthetic aperture radar image time series. In this method, geometric wavelet details are first computed with respect to the temporal axis in order to derive generalized-ratio change images from the time series. The changes are then enhanced, and speckle is attenuated by using spatial block sigmoid shrinkage. Finally, a regularized time series is reconstructed from the sigmoid shrunken change images. Some applications highlight relevancy of the method for the analysis of SENTINEL-1A and TerraSAR-X image time series over Chamonix Mont Blanc.

This paper presents a method for analyzing SAR image time series and provides initial change detection results on a time series of 11 descending Interferometric Wide Swath (IW) Level-1 Single Look Complex (SLC) Sentinel-1 SAR images over Chamonix-Mont-Blanc, France. This method is based on the Change Detection Matrix (CDM) which identifies the presence of changes in the time series. It provides a useful information to gather homogeneous samples for spatio-temporal speckle filtering and to obtain a map of change dynamics in order to reveal the temporal evolution.

This paper is dedicated to the detection of buried pipes with a ground penetrating radar (GPR). The images from GPR acquisitions also called B-scan are corrupted by clutter and noise. In order to remove these undesirable items we propose to use the properties of the curvelet transform. We're using this method as a first step of the automatic detection of hyperbola in a B-scan.

In this paper we present a processing strategy to derive glaciers' velocity fields from a complete satellite archive. All possible pairs with a specified time span are formed, submitted to the same preprocessing and matched together to derive velocity fields. All the results are then selected on a pixel-by-pixel basis based on the confidence of the offset-tracking and merged together by computing the median in a spatio-temporal neighboorhood. The method is applied to 206 images covering the Karakoram region and a period of 3 years. This allows us to estimate a velocity for over 90% of the glaciers' area and to reduce the residuals from 9.9m/yr to 2.1m/yr.

This paper proposes an approach for temporal adaptive filtering of Polarimetric Synthetic Aperture Radar (PolSAR) image time series by integrating a change detection technique. The filtering strategy is based on the detection of changed and unchanged areas derived by applying an appropriate similarity test. A time series including 7 descending fine-quad polarization RADARSAT2 images acquired from January 29, 2009 to Jun 22, 2009 over Chamonix-MontBlanc test-site which includes different kinds of change is used to validate the proposed method.

The application of Interferometric Synthetic Aperture Radar (InSAR) for detecting terrain deformation in the Amazon region is an important research tool that allows high accuracy of geophysical models based on satellite data. Amazonian forest cover and climate regime make InSAR implementation very difficult, so literature about the subject in that region is still rare. In this paper, we apply the interferometric technique to a stack of 24 Radarsat-2 images acquired from 2008 to 2010 over Manaus city (Amazonas state, Brazil) aiming to understand the terrain movement in the urban area, which has been detected by previous studies using Radarsat-1 data. The observed subsidence is estimated to be about three centimeters and occurs in the vicinities of the international airport. The phenomena can be associated with the Amazonas river flooding system, and indicates a local response from the seasonal fluctuations and Earth́s elasticity.

This letter presents an adaptive filtering approach of synthetic aperture radar (SAR) image times series based on the analysis of the temporal evolution. First, change detection matrices (CDMs) containing information on changed and unchanged pixels are constructed for each spatial position over the time series by implementing coefficient of variation (CV) cross tests. Afterward, the CDM provides for each pixel in each image an adaptive spatiotemporal neighborhood, which is used to derive the filtered value. The proposed approach is illustrated on a time series of 25 ascending TerraSAR-X images acquired from November 6, 2009 to September 25, 2011 over the Chamonix-Mont-Blanc test-site, which includes different kinds of change, such as parking occupation, glacier surface evolution, etc.

In this paper, an attempt of Alpine glacier flow modeling is performed based on a series of high resolution TerraSAR-X SAR images and a Digital Elevation Model. First, a glacier flow model is established according to the fluid mechanics theory in a simplified framework. Second, the displacement field over the glacier obtained from the sub-pixel image correlation of a series of TerraSAR-X SAR images is used to refine the model obtained previously. The comparison between the data observation and the model prediction allows for the validation of the established model. According to the obtained results, despite the simplifications made in the modeling, the established glacier flow model can provide general satisfactory results. Further investigation and improvement of this glacier flow model seem promising.

The paper provides a spatio-temporal change detection framework for the analysis of image time series. In this framework, the detection of changes in time is addressed at the image level by using a matrix of cross-dissimilarities computed upon wavelet and curvelet image features. This makes possible identifying the acquisitions of interest: the acquisitions that exhibit singular behavior with respect to their neighborhood in the time series, and those that are representatives of some stationary behavior. These acquisitions of interest are compared at the pixel level to detect spatial changes characterizing the evolution of the time series. Experiments carried out over European Remote Sensing (ERS) and TerraSAR-X time series highlight the relevancy of the approach for analyzing synthetic aperture radar image time series.

The paper addresses multi-channel complex image filtering. It provides regularization cost functions associated to non-conventional vector and matrix iv norms for promoting geometry properties. The approach is shown to be efficient for filtering PolSAR images.

This paper proposes a way of improvement of the ML texture tracking method using bootstrap sampling. A quality factor is introduced to measure the accuracy of estimation. It is based on both statistics and image processing. The bootstrap sampling uses the initial information for generating additional samples in order to enhance the available information. Some results on particular areas of a glacier are presented.

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.

In this paper, a dedicated phase unwrapping approach, taking a priori information into account and combining multi-scales local frequencies of the interferometric phase, is developed in order to get around of the discontinuity and aliasing problems. In this approach, the interferogram is characterized by local frequency of the phase. The multi-scales local frequencies of the interferometric phase are estimated and fused to the local frequency at the optimal scale. This optimal scale, the lowest resolution scale allowing phase unwrapping without aliasing problem, is determined from the a priori displacement information. The application is performed on the displacement measurement of the 2005 Kashmir earthquake. The a priori displacement information is issued from a deformation model. The advantages of this approach are highlighted by the obtained results.

This paper presents a data mining approach for describing Satellite Image Time Series (SITS) spatially and temporally. It relies on pixel-based evolution and sub-evolution extraction. These evolutions, namely the {frequent grouped sequential patterns}, are required to cover a minimum surface and to affect pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. In this paper, a specific application to fully polarimetric SAR image time series is presented. Experiments performed on a RADARSAT-2 SITS covering the Chamonix Mont Blanc test-site are used to illustrate the proposed approach.

An important aspect of satellite image time series is the simultaneous access to spatial and temporal information. Various tools allow end users to interpret these data without having to browse the whole data set. In this paper, we intend to extract, in an unsupervised way, temporal evolutions at the pixel level and select those covering at least a minimum surface and having a high connectivity measure. To manage the huge amount of data and the large number of potential temporal evolutions, a new approach based on data-mining techniques is presented. We have developed a frequent sequential pattern extraction method adapted to that spatiotemporal context. A successful application to crop monitoring involving optical data is described. Another application to crustal deformation monitoring using synthetic aperture radar images gives an indication about the generic nature of the proposed approach.

The work in this paper deals with the learning of gradual rules in the framework of data classification. Gradual rules are well suited to express constraints between numerical quantities. They are here used to constrain the shape of classes to be modeled. More precisely, it is proposed to represent convex polygon-shaped classes by means of "If-Then" classification gradual rules. The latter, learnt from training data, constitute elementary classifiers able to solve oneclass problem with two attributes. General classification problems are thus addressed by combining partial decisions of elementary classifiers. The approach is illustrated with the classification of 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.

This paper presents an original data mining approach for extracting pixel evolutions and sub-evolutions from Satellite Image Time Series. These patterns, called frequent grouped sequential patterns, represent the (sub-)evolutions of pixels over time, and have to satisfy two constraints: firstly to correspond to at least a given minimum surface and secondly to be shared by pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. Successful experiments on an optical and a radar SITS are presented.

In this paper, 2 data fusion strategies from SAR images are investigated through application to measurement of displacement field due to the Kashmir earthquake (Mw=7.6, 2005). Firstly, the 3D displacement field at the Earth's surface is retrieved by a linear inversion, using the measurements from sub-pixel image correlation and differential interferometry. In addition to the generalized least square method, a fuzzy approach is applied to represent the measurement uncertainty. Secondly, the geometry of the fault is optimized by a non linear inversion, using the same measurements. The inter-comparisons between strategies and approaches are performed in order to highlight the advantages and disadvantages of each strategy and approach.

International audience

Due to their high temporal variability, monitoring temperate glaciers by in-situ measurements is quite hazardous. The new TerraSAR-X (TSX) sensor provides high resolution SAR data which can be acquired every 11 days in the same configuration and can cover the whole surface of several glaciers in a studied area. Their potential for temperate glacier monitoring by remote sensing has to be investigated. This paper presents some early results on the Argentie¿re glacier testsite in the Mont Blanc massif to estimate the surface velocity using some texture tracking methods. After having evaluated the Differential Interferometric SAR (DInSAR) potential with TSX Stripmap data, correlation and Maximum Likelihood (ML) based methods are performed on the texture variable extracted from the Spherically Invariant Random Vectors (SIRV) estimation scheme.

Two-pass differential synthetic aperture radar interferometry processing have been successfully used by the scientific community to derive velocity fields. Nevertheless, a precise digital elevation model (DEM) is necessary to remove the topographic component from the interferograms. This letter presents a novel method to detect and retrieve DEM errors by analyzing time series of differential interferograms. The principle of the method is based on the comparison of fringe patterns with the perpendicular baseline. First, a mathematical description of the algorithm is exposed. Then, the algorithm is applied on a series of four one-day European Remote Sensing 1 and 2 satellite (ERS-1/2) interferograms.

Nowadays, there is a growing need for processing huge volumes of observation data due to the increase in size, in resolution, in spectral channel number and in acquisition frequency of remote sensing images. When data is gathered over time for a same geographical zone, this data is said to be a Satellite Image Time Series (SITS). The informational content of SITS is rich because the observed scene is described both in time and in space. In order to exhibit potential interesting spatio-temporal patterns, we propose to extract pixel-based evolutions from SITS data by using two different symbolic techniques. The first one is based on data mining techniques that aim at extracting frequent sequential patterns (e.g.,). The second one relies on the use of tries (e.g.,) for classifying pixels according to their evolution in time. Encouraging experiments on a SPOT SITS are detailed.

This article presents a new approach based on an integration of multi-attributes semantic partitions (MASP) coming from the three following attributes: an urban growth layer computed from a couple of ERS-SAR images -exploited as a vulnerability map-, a dispersion flow (DF) layer estimated from a linear DF model -used as a flood hazard map-, and a NDVI layer used as an urban/non-urban interpretation measure, in order to produce a flood risk (FR) map. Thus, input mono-attribute semantic partitions (imASP) are first defined from attributes, and their membership degree functions are built based on the fuzzy subset theory. Then imASP are selected to form MASP according to the degree of confidence given to each one to perform the flood risk (FR) and membership degrees of MASP are calculated to provide individual degrees of FR worsening (FRW). Lastly, the global degree of FRWis computed by aggregating previous individual degrees of MASP by using a fuzzy integral (FI) to achieve the resulting FR map.

This paper presents an application of the recent advances in the field of Spherically Invariant Random Vectors modelling. We propose the use of the Fixed Point (FP) estimator for deriving normalized polarimetric coherency matrices in compound Gaussian clutter. The main advantages of the FP estimator are that it does not require any "a priori" information about the probability density function of the texture and it can be directly applied on adaptive neighborhoods. Interesting results are obtained when coupling this FP estimator with an adaptive spatial support driven on the scalar span information. The proposed method is tested with both simulated POLSAR data and high resolution POLSAR data acquired over the French Alps.

Synthetic aperture radar (SAR) interferometric data offer the opportunity to measure temperate glacier surface topography and displacement. The increase of the resolution provided by the most recent SAR systems has some critical implications. For instance, a reliable estimate of the phase gradient can only be achieved by using interferogram local frequencies. In this paper, an original two-step method for estimating local frequencies is proposed. The 2-D phase signal is considered to have two deterministic components corresponding to low-resolution (LR) fringes and high-resolution (HR) patterns due to the local microrelief, respectively. The first step of the proposed algorithm consists in the LR phase flattening. In the second step, the local HR frequencies are estimated from the phase 2-D autocorrelation function computed on adaptive neighborhoods. This neighborhood is the set of connected pixels belonging to the same HR spatial feature and respecting the ldquolocal stationarityrdquo hypothesis. Results with both simulated TerraSAR-X interferograms and real airborne E-SAR images are presented to illustrate the potential of the proposed method.

This paper highlights the potential to measure temperate glacier velocities and surface characteristics by airborne interferometric and polarimetric SAR remote sensing. Indeed, a novel SAR airborne campaign took place in October 2006 over two Alpine glaciers. Simultaneously to the acquisition of repeat pass interferometric, polarimetric and multi-band data, in-situ measurements were carried out to provide useful information for the SAR synthesis, for backscattering analysis and for performance assessment. Analysis of the experimental data as well as early PolInSAR processing results regarding information extraction are presented.

SAR interferometry (InSAR) performs two acquisitions (spatially separated by the baseline) of the signal back-scattered by the resolution cell which contains height and/or displacement information. Repeat pass spaceborne interferometry provides multi-interferograms which can be used to extract such information either by combining the multi-temporal results of conventional interferometry or by a different approach based on specific targets: the coherent stable scatterers (CSS). In this paper a two-step approach is proposed to obtain specific features from multi-temporal InSAR data sets. The first step consists in extracting image attributes related to the useful information. The second step consists in merging the attributes using an interactive fuzzy fusion technique. The interactive fuzzy fusion is proposed to provide end-users with a simple and easily understandable tool for tuning the detection results. The method is applied on a data set of five co-registered ERS 1/2 tandems from the French Alps (the Mont-Blanc region), including two temperate glaciers: the Argentiere and the Mer-de-glace. The results illustrate how the end-user can combine the proposed attributes to detect the presence of CSS or distributed stable scatterers usefull for multi-temporal analysis.

Monitoring temperate glacier activity has become more and more necessary for economical and security reasons and as an indicator of the local effects of global climate change. Remote sensing data provide useful information on such complex geophysical objects, but they require specific processing techniques to cope with the difficult context of moving and changing features in high-relief areas. This paper presents the first results of a project involving four laboratories developing and combining specific methods to extract information from optical and synthetic aperture radar (SAR) data. Two different information sources are processed, namely: 1) airborne photography and 2) spaceborne C-band SAR interferometry. The difficulties and limitations of their processing in the context of Alpine glaciers are discussed and illustrated on two glaciers located in the Mont-Blanc area. The results obtained by aerial triangulation techniques provide digital terrain models with an accuracy that is better than 30 cm, which is compatible with the computation of volume balance and useful for precise georeferencing and slope measurement updating. The results obtained by SAR differential interferometry using European Remote Sensing Satellite images show that it is possible to measure temperate glacier surface velocity fields from October to April in one-day interferograms with approximately 20-m ground sampling. This allows to derive ice surface strain rate fields required to model the glacier flow. These different measurements are complementary to results obtained during the summer from satellite optical data and ground measurements that are available only in few accessible points

In this paper, a new method to filter coherency matrices of polarimetric or interferometric data is presented. For each pixel, an adaptive neighborhood (AN) is determined by a region growing technique driven exclusively by the intensity image information. All the available intensity images of the polarimetric and interferometric terms are fused in the region growing process to ensure the validity of the stationarity assumption. Afterward, all the pixels within the obtained AN are used to yield the filtered values of the polarimetric and interferometric coherency matrices, which can be derived either by direct complex multilooking or from the locally linear minimum mean-squared error (LLMMSE) estimator. The entropy/alpha/anisotropy decomposition is then applied to the estimated polarimetric coherency matrices, and coherence optimization is performed on the estimated polarimetric and interferometric coherency matrices. Using this decomposition, unsupervised classification for land applications by an iterative algorithm based on a complex Wishart density function is also applied. The method has been tested on airborne high-resolution polarimetric interferometric synthetic aperture radar (POL-InSAR) images (Oberpfaffenhofen area-German Space Agency). For comparison purposes, the two estimation techniques (complex multilooking and LLMMSE) were tested using three different spatial supports: a fix-sized symmetric neighborhood (boxcar filter), directional nonsymmetric windows, and the proposed AN. Subjective and objective performance analysis, including coherence edge detection, receiver operating characteristics plots, and bias reduction tables, recommends the proposed algorithm as an effective POL-InSAR postprocessing technique.

International audience

International audience

The Mer de Glace and Argentiegravere glaciers are located in the Mont Blanc region, French Alps. They are temperate glaciers and their velocity flow is about one hundred meters a year (~270 mm a day). This paper presents a use of synthetic-aperture radar (SAR) interferogram obtained from the two European Remote-Sensing satellites (ERS1-2) to measure the motion of Mer de Glace and Argentiegravere glaciers. We investigate whether the interferometric data are quantitatively consistent with terrestrial velocity measurements along two transverse profiles and two longitudinal profiles. Interferometric and terrestrial velocity are in agreement if a (terrestrially measured) surface-normal velocity component is properly accounted for. This suggest that both the interferometric velocities and the conversions of terrestrial data to the winter period are reliable. Finally we show that the application of repeat-pass SAR interferometry to the glaciers enable precise mapping of ice flow dynamics at a much higher level than usually obtained

When a geophysicist has to make a visual interpretation in multitemporal SAR images, it may be long and repetitive. To avoid this, an automatic object detection using multitemporal active contours is proposed in this paper. The information brought by the different images can be fused at different levels: either at the data level, the feature level or a level close to the decision level. On two data-sets, both located in French Guyana, two different strategies will be tested depending on the knowledge of the object, whether it is temporally stable or moving.

An information fusion approach based on the fuzzy subset theory is proposed in this article for the appraisal of hazard worsening factors. This approach combines attributes of vulnerability degree coming from remote sensing data. After defining the hazard, menace, vulnerability concepts, an original method is proposed for the extraction of a "distance" vulnerability degree attribute, based on the fuzzy mathematical morphology. Then, a vulnerability map is produced by combining the following attributes: the distance (to the river, to a drain, or to the coast), the altitude and an index of urban area/non-urban area. The proposed approach is illustrated in the city of Douala and results are compared to previous floodings maps.

This work presents a new method for filtering the coherence map issued from Synthetic Aperture Radar (SAR) polarimetric interferometric data. For each pixel of the interferogram, an adaptive neighborhood is determined by a region growing technique driven by the amplitude image information. Then, pixels in the derived adaptive neighborhood are complex averaged to yield the filtered value of the coherence, after performing a phase compensation step. The proposed method has been applied on airborne high-resolution polarimetric interferometric SAR images. Both subjective and objective performance analysis, including coherence edge detection, shows that the proposed method provides better results than the standard phase-compensated fixed multi-look filter and a linear adaptive coherence filter proposed by Lee et al.

Multitemporal satellite synthetic aperture radar (SAR) images are a useful source of information for geophysicists to monitor changing regions. In this paper, a new approach is proposed to extract from multitemporal SAR images two kinds of information: temporal changes (flooded areas, coastline erosion, etc.) and stable spatial features (roads, rivers, etc.). The novelty of the proposed approach is to detect simultaneously these two kinds of discontinuities. In a first step, the contrast and the heterogeneity information is extracted by a "multitemporal" application of the ratio of local means and by new three-dimensional texture parameters based on the log-cumulants. In a second step, the resulting attributes that measure the time variability or the presence of spatial features are merged. An interactive fuzzy fusion approach is proposed to provide end-users with a simple and easily understandable tool for tuning the change-detection results. The performances of the proposed attributes and fusion technique are presented on a set of seven multitemporal SAR images acquired by the European Remote Sensing (ERS-1) satellite.

Satellite SAR images acquired on repeated orbits became a useful source of information to monitor changes in many areas where optical data are rarely available. In this paper, two different approaches which take the specificity of speckle distributions into account are proposed to detect changing areas. The first one consist in detecting changes of the mean radiometry by applying in a temporal direction a conventional "edge detector". The second one consists in detecting temporally heterogeneous areas by measuring 3-dimensional texture parameters using second kind statistics. The results obtained with these two approaches and their complementarity are illustrated on a 7 image time series acquired by satellites ERS.

Trying to compute or find items on SAR images, is often hard to achieve for photo-interpreters due to the speckle. Hence, the choice of a filtering approach often appears to be a tough choice. With the large number of images acquired on an area it is now possible to use multitemporal filters. When using those kind of filters the difficulty lies in finding a trade-off between temporal and/or spatial loss. Most of the time photo-interpreters set their choice on a subjective criterion. In this paper visual interpretation performance is tested to achieve an objectivity on the choice of different filtering approaches.

We propose in this paper, a new method for change detection in urban context with multitemporal SAR images. The method operates in two steps: change measures computation between the two SAR images, and the fusion of previous change measures with an interpretation measure, used as prior information, with the help of fuzzy subsets theory. This interpretation attribute comes from an optical image, across NDVI (Normalized Difference Vegetation Index). The proposed method is applied on two ERS-SAR images and one multispectral optical SPOT (2001) image of the urban area of Douala city, in order to appreciate changes between (1994) and (1999). The method allows the global change analysis in urban context at a pixel scale and at an area larger than a pixel.

This paper presents an almost unsupervised fusion algorithm on linear features (LF) extraction in synthetic aperture radar (SAR) interferometric data, in particular for mangroves/shorelines and thin internal channels. The spatial information on LFs is first extracted in the coherence image, where they are wider and more visible: water regions (in particular thin internal channels) are dark areas (low coherence) due to the temporal decorrelation of backscattering signals in these and surrounding regions, whereas conventional vegetation regions are brighter areas (high coherence). These approximate locations of LFs are further refined by using the edge map coming from a semantic fuzzy fusion of the coefficient of variation (CV) and the ratio of local means (RLM) measured in the amplitude image. The final detection of LFs is then performed by merging the two fuzzy inputs: the spatial information and the edge location map. The membership degree statistics of CV and RLM semantic fusion measures are introduced in order to illustrate the location detection ability. The originality of this method in comparison with conventional approaches is in the fusion scheme that follows the interpreter behavior by using first the coherence image for a fuzzy detection where thin LFs are more visible, but have low location accuracy, and then the amplitude image where they are poorly visible, but with higher location accuracy, to obtain improved results. A quantitative performance evaluation is also presented. The method has been applied on real interferometric SAR images from European Remote Sensing satellites over the western part of Cameroon.

Multitemporal synthetic aperture radar (SAR) image filtering is a useful preprocessing step for many applications that require speckle reduction. Several multitemporal filters are now available with very different characteristics. In this paper, the performance of three multitemporal filters is assessed with respect to statistical and operational criteria. Statistical criteria include measures of bias, noise reduction, and preservation of both spatial and temporal information. Operational criteria evaluate the accuracy of manual detection of geographical features such as points, lines, and surfaces. This study was carried out with the help of ten photointerpreters. It uses a set of seven multitemporal SAR images from the European Remote Sensing 1 (ERS-1) satellite. It provides guidelines to select multitemporal filters according to the application and the subsequent processing.

The contribution of this paper concerns the detection of changes in multi-temporal satellite SAR (synthetic aperture radar) images by a fuzzy fusion of attributes extracted from the images with a-priori map-based information. The proposed approach is based on a linguistic description of the attributes and of the relations between them that are provided by geophysicists. The fusion system is cooperative thanks to a graphical user interface that allows one to visualize reference areas in the attribute space and to easily adjust some parameters of the attribute fusion (rules, membership functions). Some detection results of small deforested areas are presented.

Presents a new method for filtering the coherence image issued from an interferometric pair. The basic idea is to determine for each pixel an adaptive neighborhood with respect to the amplitude information. Then, complex averaging is performed using values of pixels in the determined neighborhood to derive the filtered coherence value. It is shown that the proposed technique performs better than the standard fixed-neighborhood filtering technique, both objectively and subjectively.

This paper presents a method based on fuzzy fusion to improve feature extraction in satellite SAR images. Two road detectors are applied on SAR images and their output is merged interactively. Experimental results are presented with ERS1/2 SAR images in different configurations including ascending/descending orbits and georeferenced images.

In multipass spaceborne synthetic aperture radar (SAR) interferometry, the two acquisitions often present low correlation levels and very noisy phase measurements that are incompatible with automatic phase unwrapping. Instead of dealing with many residues due to erroneous-wrapped phase differences, the authors propose to use the local frequency as measured by a spectral analysis algorithm presented in a previous paper, E. Trouve et al. (1996). For this purpose, the authors present two conventional unwrapping algorithms, one local and the other global, which they revisit to benefit from the robust focal frequency estimates. For a local approach based on path-following techniques, they use the frequency estimates in a slope-compensated filter that extend the complex averaging up to a sufficient number of looks to eliminate residues due to the noise. Then they connect residues due to noninterferometric features along mask components resulting from the detection of layovers and uncorrelated areas. For a global approach, such as the weighted least-squares methods, they demonstrate that the use of noisy discrete phase gradient leads to a biased solution. To avoid this drawback, they propose to use the local frequency estimate and associated measure of confidence as phase gradient and weight. Results are presented on both topographic and differential interferograms obtained from the ERS-1 European radar satellite over various landscapes and the displacement field of the Landers 1992 earthquake.