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Missing data is a critical pitfall in the investigation of remotely sensed displacement measurement because it prevents from a full understanding of the physical phenomenon under observation. In the sight of reconstructing incomplete displacement data, this letter presents a data-driven spatiotemporal gap-filling method, which is an extension of the expectation–maximization-empirical orthogonal function (EM-EOF) method. The presented method decomposes an augmented spatiotemporal covariance of a displacement time series into EOF modes and then selects the optimal set of EOF modes to reconstruct the time series. This selection is based on the cross-validation root-mean-square error and a confidence index associated with each eigenvalue. The estimated missing values are then iteratively updated until convergence. Results on displacement time series derived from cross correlation of Sentinel-2 optical images over Fox Glacier in New-Zealand’s Alps show that the reconstruction accuracy is improved compared with the EM-EOF method. The proposed extension can tackle challenging cases, i.e., short time series with heterogeneous displacement behaviors corrupted by a large amount of missing data and noise.

An iterative method, namely expectation maximization-empirical orthogonal functions (EM-EOF) is proposed for the first time to retrieve missing values in synthetic aperture radar (SAR) displacement time series. This method decomposes the temporal covariance of a displacement measurement time series into different EOF modes by solving the eigenvalue problem, and then selects the optimal number of EOF modes to reconstruct the time series. After an appropriate initialization of missing values, the EM-EOF method performs: 1) a cross-validation root-mean-square error (cross-RMSE) minimization to find an estimate of the optimal number of EOF modes used in the reconstruction and 2) an iterative update of missing values which gives the best estimate of missing data points according to the cross-RMSE. Synthetic simulations have been first performed to highlight the efficiency of EM-EOF in the case of various displacement signal complexities and different types of noise and gaps, and a thorough error analysis has been conducted to determine the sensitivity of the method to signal-to-noise ratio (SNR), quantity of gaps, and type of noise and gaps. Then, EM-EOF is applied to three displacement measurement time series computed from Sentinel-1 A/B SAR images: two interferograms time series over Gorner and Miage glaciers, and one offset time series over the Argentière Glacier covering a period extending from September 2016 to December 2017. Both synthetic simulations and real data applications demonstrate the ability of EM-EOF to retrieve missing values, even in the cases of frequent data gaps, limited size of the time series, and spatio-temporally correlated noise and gaps.

An iterative method, namely extended EM-EOF (Expectation Maximization - Empirical Orthogonal Functions) is proposed to retrieve missing values in satellite derived displacement time series. The method constructs the spatio-temporal covariance of a displacement time series, decomposes it into different EOF modes by solving the eigenvalue problem and then selects an optimal number of EOF modes to reconstruct the time series based on cross validation errors. The latter are also used as convergence criterion in an EM-like algorithm. A confidence index associated with each eigenvalue, estimated from the eigenvalue uncertainty, is introduced as a metric for refining the estimated optimal number of EOF modes. Results on simulated displacement time series perturbed by spatially correlated noise demonstrate the potential of extended EM-EOF to impute spatio-temporal missing values even in case of a reduced size of time series.

This paper introduces a method for offshore oil slick detection. At present, Synthetic Aperture Radar (SAR) is an image acquisition technology useful for oil slick detection in all weather conditions. It is used to carry out the detection, with notable limitations under certain conditions (surfaces, weather conditions). Manual SAR images analysis is expensive and, given the increasing amount of data collected from available sensors, automation becomes mandatory. To achieve this objective, instance object detection relying on deep neural networks is interesting to adapt to the data variability. Relying on such an approach, this article explores the capabilities of generalizing the detection of slicks on large datasets using the Mask-RCNN model. A detailed performance analysis is established in two complementary directions: (i) the impact of the SAR image characteristics(sensor, geographical areas, lookalike presence), (ii) the impact of the neural network architecture, transferred capabilities and training procedures. The main findings of this analysis show that Mask-RCNN features promising performance for pollution detection.

An iterative method, namely EM-EOF (Expectation Maximization-Empirical Orthogonal Functions) is proposed for the first time to retrieve missing values in InSAR displacement time series. The method decomposes the temporal covariance into different EOF modes by solving the eigenvalue problem, and then selects an optimal number of EOF modes to reconstruct the time series. After an appropriate initialization of missing values, the proposed method performs (i) a cross-validation error minimization to find an estimate of the optimal number of EOF modes used in the reconstruction and (ii) an iterative update of missing values which gives the best estimate of missing data points according to the cross-validation error. Results on a time series of Sentinel-1 A/B unwrapped interferograms over the Gorner glacier from November 2016 to March 2017 demonstrate the high efficiency of the proposed method at retrieving missing values and denoising the time series, even in case of time series with limited size and spatio-temporally correlated gaps.

The problem of learning fuzzy rule bases is analyzed from the perspective of finding a favorable balance between the accuracy of the system, the speed required to learn the rules, and, finally, the interpretability of the rule bases obtained. Therefore, we introduce a complete design procedure to learn and then optimize the system rule base, called the precise and fast fuzzy modeling approach. Under this paradigm, fuzzy rules are generated from numerical data using a parameterizable greedy-based learning method called selection-reduction, whose accuracy-speed efficiency is confirmed through empirical results and comparisons with reference methods. Qualitative justification for this method is provided based on the coaction between fuzzy logic and the intrinsic properties of greedy algorithms. To complete the precise and fast fuzzy modeling strategy, we finally present a rule-base optimization technique driven by a novel rule redundancy index, which takes into account the concepts of the distance between rules and the influence of a rule over the dataset. Experimental results show that the proposed index can be used to obtain compact rule bases, which remain very accurate, thus increasing system interpretability.

This brief derives a 2-D spectrum estimator from some recent results on the statistical properties of wavelet packet coefficients of random processes. It provides an analysis of the bias of this estimator with respect to the wavelet order. This brief also discusses the performance of this wavelet-based estimator, in comparison with the conventional 2-D Fourier-based spectrum estimator on texture analysis and content-based image retrieval. It highlights the effectiveness of the wavelet-based spectrum estimation.

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.

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.

International audience

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.

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.

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

While in the continuous case, statistical models of histogram equalization/specification would yield exact results, their discrete counterparts fail. This is due to the fact that the cumulative distribution functions one deals with are not exactly invertible. Otherwise stated, exact histogram specification for discrete images is an ill-posed problem. Invertible cumulative distribution functions are obtained by translating the problem in a K-dimensional space and further inducing a strict ordering among image pixels. The proposed ordering refines the natural one. Experimental results and statistical models of the induced ordering are presented and several applications are discussed: image enhancement, normalization, watermarking, etc.

International audience

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.

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 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.

The paper proposes a spatial domain watermarking scheme for image authentication and, in case image content has been changed, for localization of the altered area. A pseudo-random set of pixels is selected by using a secret key and is marked with a visually meaningful watermark by exact histogram specification. The exact recovery of the watermark authenticates the image. Specially designed watermarks are used to locate the altered area. Image visual aspect as well as image histogram are preserved. The approach addresses gray level and color images as well.

For graylevel images, the histogram based watermarking approach considers the image histogram as its signature and, by exact histogram specification, images are marked accordingly. This paper extends to color images the histogram based watermarking. Image marking is performed in the hue, saturation, intensity (HSI) space by creating notches in the intensity component histogram. The number of notches and their locations are the features which define each watermark. The detection is blind and of considerably low complexity. Robustness of the proposed method is investigated-tests performed using StirMark show that marking with notches ensures high robustness against many attacks: JPEG compression, geometric distortions (cropping, scaling, rotation, row and column removal, translation, etc.,), linear and nonlinear filtering.

In this paper, a 3D nonstationary local distance operator is introduced. This kind of operator is adapted to the measurement of distances between objects sampled on nonuniform grids. This operator can be regarded as a nonstationary version of local distance operators adapted to parallelepipedic (noncubic) grids. The principle of coefficient computation and spatial adaptation are given. An application to dissimilarity measurement between color images is presented. We compare the nonstationary local distance operator with two stationary versions: 1) images are sampled with the lowest sampling step; and 2) the local operator is designed according to the average voxel size.

The problem of detection of road networks in Synthetic Aperture Radar (SAR) images is addressed. This task is important for the registration of SAR images to existing maps and for perfect superposition of multitemporal SAR images. SAR image analysis is a difficult task because of speckle noise and textures and low signal to noise ratios. The paper focuses on the comparison of filter performances in terms of road extraction. The study points out the interest of nonlinear adaptive filtering techniques. On the other hand, it is shown that techniques dedicated to speckle noise filtering are not always the most suited to road network extraction.

The paper investigates the use of exact histogram specification for robust watermarking. A class of watermarks are selected such that the presence of certain groups of consecutive gray levels is considerably reduced with no visual degradation of images. The proposed technique is proven to be very resistant to many intentional and unintentional attacks.

The paper investigates the definition of strict ordering relations on digital graylevel images. The information needed for strict ordering is extracted by a vectorial operator in the local context of each pixel. The paper focuses on ordering which refines the natural ordering defined on the lattice of integers: pixels having greater graylevel preserve inequality while equal graylevel pixels are differentiated. Applications in exact histogram specification, segmentation and image normalization are discussed.

In this paper, we introduce a method aimed at improving the segmentation results obtained with complex natural images. It is based on the integration of a set of image segmentation maps. This technique allows the redundancy between some primary segmentations to be extracted. Our approach is mainly region-oriented. It is based on a fuzzy description of the segmentation by means of a mechanism associating regions of different maps. Introducing a dissimilarity measurement allows a so-called segmentation filtering to be performed. This approach turns out to be very efficient in the case of natural complex images. It can be regarded as an extension of nonlinear filtering techniques.

Many skeletonization methods based on distance transformation have been proposed. They are generally designed for square grids. However most industrial vision systems digitize images on rectangular grids. These images are then resampled to obtain square pixels. In this paper we propose a skeletonization algorithm directly adapted to rectangular grids. We use the efficient skeletonization method of Arcelli and Sanniti di Baja (1993), based on the Euclidean distance transform, and we adapt it to chamfer distances computed on rectangular grids. We propose some modifications to this algorithm. Some results are given and discussed.

In the task of segmentation of some complex pictures, it is often difficult to obtain satisfactory results using only one approach of image segmentation. The tendency toward the integration of several techniques seems to be the best solution. The authors introduce a new approach of image segmentation, which tends to combine several sources of knowledge about the image in a way to produce better segmentation results. First, they try to locate germs that are homogenous by means of a region-region cooperative process. A region growing process is then applied to these germs in order to find the region borders. This process is controlled, on the one hand, by the germs' parameters, and on the other hand by a gradient information obtained by a simple edge detector. Finally, a region merging step is applied on the extracted regions in order to reconstruct regions that have been split by the germs' extraction process. This method has given good segmentation results over several complex natural images.>

DNA sequencing consists of retrieving the arrangement of nitrogenous bases which contain the genetic information of living systems. Most of sequencing devices are based on the analysis of electrophoretograms. Thus the 3D structure of DNA molecules is represented by a 2D grey level image. Because of the physical and chemical processes involved, resulting images are noisy and blurred. The objects to be localized in the image are thin and stretched structures. In this paper, we introduce a directional adaptive filter aiming at preserving the shape of the structures and at reducing noise and artifacts. This filter selects an appropriate sub-window and operates an order filtering in it. Some statistical properties of this filter are studied. Experimental results are then presented.>

Based on the cross-correlation of two signals, the speed measurement method presented in the paper is to be used on materials whose structure is stable (railway, papersheet...) or not (turbulent flows). There is not only a time delay between the signals, but more generally a filtering effect which may be regarded as linear. In these conditions the theoretical characteristics (bias, variance) of the estimation are given. They are compared with simulation results. The influence of the computation rapidity of the electronical circuit on the quality of the estimation is studied.