SAR time series are powerful assets for forest monitoring. In recent years, they were involved in various classical forest applications such as forest mapping [9] . These applications largely benefited from the advances of Deep Learning, particularly Unsupervised Learning, using Convolutional Autoencoders in applications such as wildfire detection [4] . Not only did purely temporal approaches offer high prediction performance compared to spatiotemporal variants, but the unsupervised autoencoder rivaled its supervised counterparts. The monitoring of forests also involves the detection of under-canopy targets, which could disturb protected environments. The literature mostly relies on classical SAR approaches PolSAR change detection [8] . A recent shift towards the usage of SAR time series displayed promising performance [10] . Thus, to fully exploit the potential of multi-temporal SAR imagery, this paper proposes the usage of unsupervised Deep Learning, particularly the Convolutional Autoencoder, to detect under forest cover objects.

In this work, we investigate the challenges of implementing a large-scale rainforest mapping with Sentinel-1 short time series. A frequent and accurate monitoring of these ecosystems is of utmost importance in the context of environmental policy-making. In particular, we propose to combine additional descriptive features with deep learning to mitigate the effect of seasonal components on SAR backscatter and interferometric coherences over a year of acquisitions in the Amazon forest. Preliminary analyses suggest that precipitation patterns might play a key role in how discernible land cover classes are with respect to the radar-based input data. Moreover, our findings show that such effects may vary in different regions of the rainforest, so that different configurations of ancillary features might be necessary to achieve a large-scale model able to generalize in both seasonal and regional dimensions.

The C-Band radiometric signature of boreal forests is highly seasonal, with apparent correlations to temperature changes. Within these seasonal components, we assume that information related to tree height can be extracted. We apply a one-dimensional Convolutional Neural Network to assess this assumption, intending to retrieve tree height measured by Airborne Laser Scanning from C-Band Sentinel-1 time series. A study site in the Parc National des Grands Jardins, in Québec, Canada, was selected for this analysis. Prediction-wise, we reach an R2 score of 0.45 and an RMSE of 1.84m, following a 4-fold cross-validation, which exhibits a non-negligible influence of the tree height parameter on boreal forest radiometric response in C-Band Synthetic Aperture Radar, despite the presumed fast saturation of this wavelength, when observing forested environments. In addition to performance metrics, we use a gradient-based explainability tool to diagnose the most contributing periods of the input time series to predict tree height to better correlate the seasonal conditions of this parameter’s influence on the forests’ radiometry.

Temporal and spatial information is exploited in order to improve road detection performance in a forested area. Speckle is filtered using an FBR-based SAR time-series speckle filter, thus preserving temporal information. The additional spatial information is provided by the ascending and descending directions of acquisition of the satellite. Instead of a detection being made if the pixel value is lower than the threshold, we require the threshold to be met for every date in the time-series, and from both directions at once. This corresponds to both a temporal and spatial (generally East-West) stability requirements, and tends to reduce false positives as forest is not a stable environment. The technique was applied on a Sentinel-1 dataset over an area in northern Belize.

In this paper, we propose an extension to an existing permittivity inversion scheme by taking into account double bounce scattering from a rotated dihedral geometry. By considering the rotational aspect, we aim to broaden the applicability of the permittivity inversion scheme for rotated double bounce signatures present in Synthetic Aperture Radar (SAR) imagery. The validity is first demonstrated by invoking to the characteristics of the co-pol ratio through simulations. The inversion scheme is then tested on real airborne SAR data to further validate its effectiveness in real-world scenarios.

This paper presents a simplified scheme for complex permittivity inversion of a double bounce scattering using polarimetric measurements. A forward scattering model is employed to establish the relationship between the observed far field and the underlying permittivity profiles. An inversion scheme based on an approximation is then developed to separately obtain the complex permittivity values corresponding to the two surfaces that constitute a dihedral structure. Electromagnetic simulations demonstrate the effectiveness of the proposed method in accurately recovering the complex permittivity values. Using real airborne data provided by ONERA, the proposed scheme was then utilized to test the possibility of extracting the complex permittivity profiles from double bounce scattering signatures in PolSAR imagery. The obtained complex permittivity profiles exhibit a correlation with previous studies, indicating the reliability of the inversion scheme to study the inverse double bounce scattering problem.

This paper introduces a tool for explaining the latent space generated by applying convolutional autoencoders to satellite image time series, entitled Grad-SLAM. We rely on backpropagated gradient interpretation combined with network activation localization. We use the proposed formula for multiple layers of the encoder, then scale and merge the results to generate a single date contribution metric for the generation of the latent space. We illustrate the potential of this method with the study of the unsupervised classification of agricultural Sentinel-1 time series. We show that critical characterizing dates for unsupervised retrieval of a given class are conditioned by the crop type’s radiometric signature and class count. We also present how Grad-SLAM can be used to enhance the understanding of unsupervised classification confusion.

International audience

Methods using data from different sensors are increasingly used, with the rise of machine learning especially. This article presents a technique based on differential entropy, a concept in information theory that measures the randomness of a distribution. This method can take input data from different sensors. For example, by combining SAR and optical images, one can hope to combine the quality of optical images with the availability of SAR images, capable of seeing at night and through clouds. Examples of this technique using both optical and SAR images are presented in this article, showing the advantages and drawbacks of this approach. The different points to be studied will then be discussed.

It is well known in SAR imagery that the materials of the objects illuminated by a transmitter have a substantial impact on the signal collected by the receiver. It is also widely admitted that this effect depends on the polarization of both emitted and received electromagnetic fields. However, this impact has been poorly investigated, probably because of the lack of ground truth information. We propose in this article to summarize our recent findings on how the materials affect the radar response and how we can use this, both for better understanding the radar phenomenology and for permittivity retrieval.

We extend a technique to retrieve effective permittivities developed for real aperture radar to be used in a Synthetic Aperture Radar (SAR). It can remotely retrieve complex permittivities using polarimetric radar signals from a double bounce. We include the technical hurdles in the extension and then present the first results of a test using airborne X-band data obtained by ONERA.

We present a fully unsupervised learning pipeline, which involves both a projection method and a clustering algorithm dedicated to the pixel-wise classification of multitemporal SAR images. We design a Convolutional Autoencoder as the method to project our time series onto a lower dimensional latent space, where semantically similar temporal signals are placed close together. The additional use of convolutional layers as feature extraction steps allows us to exploit the sequential nature of time series, exhibiting higher representation performance than fully connected layers. The extracted clusters can encapture different semantic levels to either separate classes or extract outlying temporal signals. The application of this method to crop-types mapping enables the extraction of major crop-types within a scene, without supervision. In a labeled context, this method also allows for the extraction of outlying profiles which can lead to the discovery of mislabeled time series.

The Doppler frequency signature and the cadence frequency of a couple of people moving simultaneously into the forest are presented in this paper. A brisk walk and a run have been analyzed in L-band. The results are based on measured data that have been collected by a bistatic radar working in continuous wave (CW). The purpose of the analysis is to assess the reliability of the short-time Fourier transform (STFT) and of the cadence frequency diagram to distinguish two physical activities performed simultaneously in a forested area and recorded at 1 GHz. Obstructing trees and small movements of the undergrowth largely impacted the spectrogram, making it difficult to interpret. Instead, the cadence frequency looks reliable for detecting the two activities and distinguishing them from the surrounding vegetation.

Temporal Convolutional AutoEncoders are used as feature extractors to project time series onto a latent space where similarity detection can be easily performed. This model can generate accurate descriptors of the temporal profile of the input time-series. We apply this algorithm to PolSAR S1 uncoherent SAR time series where the model learns highly discriminative data representations. This reduction method is compared to others such as PCA or Temporal Averaging and is shown to outperform them when leveraging the learnt representation using K-Means clustering.

The spatial and temporal diversity provided by Sentinel-1 SAR images is used to detect archaeological structures. The temporal mean over a year suppresses speckle without reducing spatial resolution. The combined use of the ascending and descending orbits makes it possible to highlight man-made features.

Road detection is a well-known subject in optics. For example, it is now possible to perform a road extraction and calculate a travel time using multiple look angles optic data [1]. In SAR, road extraction is not new neither [2], [3], even if it is less developed. When it comes to detection in SAR images, it is most of the time about detecting a small target with high intensity, and if the data set is temporal, the target is punctual and present at few dates. However, it is different here for roads, as they can be thin but are often long, have low radiometry, and are permanent in time. This article shows that it might be possible to characterize roads compared to their environment using temporal methods. A proposition for road extraction based on these road's characterizations is shown, and ideas about improving the extraction are presented.

The Doppler frequency signature of a man walking into the forest is presented in this paper. The results are based on measured data that have been collected by a bistatic radar working in continuous wave (CW) in the UHF-band. VV and HH polarizations have been employed for our study. The spectrograms have been generated at 1 GHz and 435 MHz by using a Short-Time Fourier Transform (STFT). The purpose of the analysis is to highlight the impact of both the chosen frequencies and the VV and HH polarizations on the Doppler spectrum of a target moving in a cluttered environment. The Doppler signal has been detected at both used frequencies, but it has appeared strongly fragmented by using the VV polarization nevertheless. The HH polarization instead has proved to be more efficient in detecting the Doppler return of the subject moving around the obstructing trees.

Target detection is an important part of research in SAR. However, the detection of small targets has always been complicated as most of the methods are spatial and based on the use of information from neighbouring pixels to characterise more precisely the studied pixel. In addition, data are often filtered to reduce the noise which results in an additional loss of spatial information. In the frame of small targets detection, this loss has to be avoided and to do so we propose to use temporal methods. Instead of using neighbouring pixels, one can use the different values of the pixels over time which allow not to wipe out small targets. The aim of this preliminary study is to show the advantages of temporal methods over spatial methods and to highlight the points to be further investigated in temporal methods. Promising results on intensity and entropy have been obtained.

Man-made structures, such as buildings, bridges, dams, etc. can be considered as quite constant along time, regarding their position and orientation of course but also for their size and composition. Man-made targets, such as vehicles, exhibit also some constant features, as dimensions and materials. However their position and orientation may have changed between two acquisitions. We propose in this study to investigate the potential benefit of time series of polarimetric SAR images for detection of man-made structures and targets. Two strategies are proposed and tested for these cases.

This article illustrates the benefit of crosspolarization, both in intensity and in phase, to discriminate urban areas and vegetation when there is an ambiguity while using the Pauli decomposition. This ambiguity occurs when buildings are rotated with respect to the sensor trajectory. In this case, we show that because the dissymetry is not of the same nature (deterministic vs quasi-random), it is then possible to discriminate these two environments.

A preliminary Doppler analysis on a man walking in free space at 1 GHz is presented in this paper. Firstly, the back-scattered response of the moving target has been provided by a simulation tool based on physical optics (PO) theory. Then, a short-time Fourier transform (STFT), a reassignment spectrogram (RE-Spect) and a Wigner-Ville distribution (WVD) have been employed to the data, in order to characterize the micro-Doppler signature of the walking human body. The results are based entirely on numerical tests. The investigation on the time variations of the Doppler spectrum of moving targets at lower frequencies is of interest for the emerging radar applications devoted to the detection and tracking of people in highly cluttered environment.

Wild fire is an inflexible threat for the environment equilibrium and human lives, consequently it is vital to study its behaviour for the collective interest and scientific knowledge. Satellite and airbone SAR represents an interesting alternative to optical sensors when the conditions are not favorable. For instance, with presence of clouds, heavy rain or smoke the efficiency of optical sensors decrease drastically for imagery purpose. Since SAR sensors overcome these issues, it is interesting to evaluate their capability to detect fire burn-scars as well as active fires. The NASA Jet Propulsion Lab provide airbone multitemporal L-band full polarimetric data for several areas in USA and South America, in particular, some data are available in California where countrysides are know to be affected by regular significant fires. Depending on the severity of the fire and the vegetation, different physical mechanisms might be affected. The aim of this study is to evaluate the benefit of Pauli basis decomposition in the frame of Change Detection algorithm for burn scar detection.

Confusion between the polarimetric radar signatures of rotated buildings and vegetation has been widely studied to compensate for the effect of rotation. We propose in this study to have a new look on this problem. To do this, we have selected different areas in the San Francisco bay and in New Orleans. These selected zones present different orientations with respect to the sensor illuminating the scene, RADARSAT-2 (C-band) for San Francisco or UAVSAR (L-band) for New Orleans. The variations with the orientation angle of the VV/HH and HV/HH responses collected over these areas are almost identical whatever the sensor. These two quantities first grow and rapidly reach a plateau, at a level which is similar to the responses of forests we collected throughout literature. Actually, it seems that, when the rotation angles grows, the polarimetric radar responses of urban areas tend to limit values which are similar to the radar responses of forests.

In this paper the development of a new method to extract the two complex relative permittivities of a dihedral structure composed of two surfaces in monostatic configuration is exposed. Provided a dominant double bounce mechanism, we only need in theory the copolarised far-field measurements HH and VV at one incidence angle. As the permittivity of natural material is strongly dependent on its moisture content, the method is used here to infer the water content of the soil. To do so we worked with simulated data using the Geometric Optic method implemented in FEKO. First results show that some incidence angle range has to be preferred to accurately determine the permittivity of the horizontal surface.

The classification of urban environments poses significant challenges for polarimetric synthetic aperture radar (SAR), since rotated urban blocks violate the assumption of reflection symmetry held by many decomposition approaches. Vast sections of cities appear to be dominated by volume scattering due to the introduction of coherent depolarization. Attempts to model this slanted double bounce must contend with the fact that dihedrals rotated about the vertical axis do not support significant backscatter and require a rough ground surface to affect a return path. In this paper, the concept of an effective dihedral is introduced, with one plate coincident with the building wall and one plate associated with some ground facet, oriented so as to support double bounce. The rotation of this effective dihedral can be readily related to the polarization orientation angle and used to quantify co-pol and cross-pol phases. Theoretical results are confirmed with L-band and C-band polarimetric SAR imagery of San Francisco, as well as by broadband laboratory experiments with modeled buildings. In addition, the utility of using cross-pol phase to classify urban regions in the Los Angeles basin is demonstrated with ascending and descending dual-pol Radarsat data.

Buildings that are rotated with respect to the sensor trajectory could be erroneously classified as vegetated areas in the Pauli basis, and subsequently in many decomposition theorems despite the considerable amount of work done to solve that issue. This misjudgement is linked to the high level of their cross-polarized contribution. Using electromagnetic simulation tools and image analysis, we study the value of these cross-polarization components. We show that forested areas and cities exhibit significantly different cross-polarization levels; indeed, the origin of these components is actually distinct. Based on that, to discriminate between the two environments, we introduce an extension to the Pauli basis where the cross polarization is split into two classes, one for rotated dihedrals and the other for random scatterers. This approach is then tested on two synthetic aperture radar images: the first acquired at C-band using RADARSAT-2 over Downtown San Francisco and the second using RAMSES at X-band over an industrial area near Paris.

The double bounce mechanism is the main characteristic in urban areas. This paper describes two physical phenomena that can decrease it (and even suppress it) and thus alter the radar analysis of cities.

When the incidence angle is equal or about the Brewster's angle, a lossy medium favors the transmission of VV polarization. As a result, there may be a strong difference between the co-polarization components of the reflected waves. For a dihedral-type scattering mechanism, this effect is enhanced and occurs for a wider range of incidence angles. This situation - typically present in urban areas - may induce significant changes in the polarimetric signatures.

This letter focuses on the analysis of layover effects in interferometric synthetic aperture radar (SAR) data of urban areas. In particular, we derive two formulations to express the backscattering coefficient and the interferometric coherence in this case. These equations show that the backscattering coefficient and the interferometric coherence in layover areas can be seen as a combination of the backscattering coefficients and interferometric coherences of the individual scattering mechanisms. These formulations are then tested on interferometric SAR (InSAR) data and analyzed statistically.

The objective of this paper is to propose an alternative measurement device at optical scale to help understanding of bistatic polarimetric SAR images of forests. The device is employed to measure nanoscale trunk forests with a 106scale factor. Considering the scale invariant rule in electromagnetic scattering this device would enable to predict the Mueller matrix of a whole tree forest for P band radar measurements. The device presents the advantages to be low cost and to get a complete set of bistatic configurations at once. Moreover, the measurements are very fast and would potentially be done on infinitely diverse and well controlled forest structures. Multiplicative decompositions of the Mueller matrix are presented. We underline the trunk density influence for an entire scope of bistatic configuration.

We develop a new synthetic aperture radar (SAR) algorithm based on physical models for the detection of a man-made target (MMT) embedded in strong interferences (trunks of a forest). These physical models for the MMT and the interferences are integrated in low-rank subspaces and are based on scattering and polarimetric properties. Several images, called subspace SAR images, can be generated and combined considering these subspace models. We then propose a new approach for target detection and interference reduction based on the combination of SAR subspace images. We show that our SAR algorithm outperforms the classical SAR imagery algorithm on both simulated data and real data in the context of foliage penetration detection.

In this paper, our concern was to help understanding the different scatterers responses in an interferometric image of a urban area by studying the different mechanisms that can occur inside the urban canyon. This study relies on a geometric code we specifically developed for this kind of scene. In this example, because of the reflections on the walls, we highlight the impact of the Brewster angle on the interferometric signature. For this reason, we recommend the use of different polarization and incidence angles for urban areas analysis of InSAR image.

In this paper, we present numerical simulations of forest scattering in bistatic configuration. The aim is to compare the influence of the structure of the forest on typical measurement parameters in a bistatic and polarimetric framework. Bistatic configuration pledges advantages of cost and stealth. The scattering model COBISMO has been developped to simulate such configurations. Simulations are performed on four forest structures with different dispositions of branches. Results show an important influence of branch inclinations but not a real influence of their position.

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.

In this paper the contribution of the polarimetric information to discriminate signal from interference subspaces is presented. By using only one single polarization, it is shown that the target and the interferences responses are similar; the reduction of false alarms due to the trunk is there fore not possible. The use of double polarization allows to discriminate the target response from the interferences ones. False alarms can then be reduced. These results show that the polarimetric information greatly contributes to increase the difference between the signal and the interference subspaces. We expect to include the cross-polarizations (HV and VH) to improve the encouraging results presented here.

We develop a new SAR processor based on several orthogonal projections.We take into account the scattering properties of the target and the interferences by using subspace models. To detect the target without detecting the interferences, we process images from the orthogonal projection of the received signal into the target subspace and from the orthogonal projection of the received signal into a part of the interference subspace. We can combine these two images to firstly detect the target and to secondly reduce interference. This new SAR processor is applied to realistic simulated data for FoPen (Foliage Penetration) application.

We develop a new SAR processor based on oblique projection. We take into account the scattering properties of the target and the interferences by using subspace models. To detect the target and to reject the interferences, we process images with the oblique projection of the received signal into the target subspace along the interference one. This new SAR processor is applied to realistic simulated data for FoPen (Foliage Penetration) application.

This paper deals with three new polarimetric SAR processors based on subspace detectors. These algorithms aim at using models with physical and polarimetric scattering properties not exploited by the isotropic point model. These processors are implemented by computing the corresponding target subspaces. Results on simulated data with realistic targets show the interest of these new processors.

This paper deals with two new SAR processors based on CFAR subspaces detector. These two algorithms aim at improving man made target detection performances in a forest clutter by using electromagnetic scattering models. The implementation of the two detectors is described. An application on simulated data shows the interest of the two methods.

As a long term objective we would like to define the required conditions to detect oil spills, using High Frequency Surface Wave Radar (HFSWR). Assuming that the presence of oil spills on the sea surface modifies the surface tension which in turn affects the dynamics of the sea, we would like to determine the minimal surface tension which makes this phenomenon observable on a Doppler spectrum. For this purpose we have developed a simulator based on a realistic modeling of the sea and of its temporal variation as well as on an exact modeling of the interactions between the electromagnetic waves and the environment. We have chosen to implement a full wave model called ELSEM3D, developed by ONERA. This tool is based on the Method of Moments (MoM) which can be accelerated by the Fast Multipole Method (FMM). The issues of such a modeling are numerous: computational time due to the number of temporal realizations required to generate a Doppler spectrum and the dimension of the scene. In this paper, we will discuss the issues raised by this study and outline how we can overcome them. We will also present some simulated Doppler spectra for different sea states and radar configurations.

The objective of this paper is to examine the link between the attenuation coefficients and the interferometric phase center heights, for several frequencies from P- to L-band, and to study the extent to which it depends on the canopy architecture and description. This paper relies on the use of a coherent and full polarimetric scattering model, which simulates the fields backscattered by a forested area. In the first part, we study the behavior with a frequency of the interferometric phase center heights, and in the second part, we focus on the attenuation coefficients. Then, we compare the behaviors of these two quantities, and we propose to empirically derive a relation between these two quantities and the mean forest height. Finally, we investigate if a change in the initial forest or radar configuration has an impact on the determination of this relation.

This paper deals with the capability of a SAR processor based on a subspace detector to get better performance than a classical SAR processor for Man Made Target (MMT) detection in a forest. The new algorithm aims at using new models, different from the isotropic point one commonly used in SAR processors. The implementation of the Subspace Detector SAR (SDSAR) algorithm is described and detection performances between a classical SAR (CSAR) algorithm and the SDSAR one are compared when detecting a MMT in white Gaussian noise and in a simulated forest.

The objective of this paper is to examine the link between the attenuation coefficients and the interferometric phase center heights, for several frequencies from P-band to L-band, and to study in what extent it depends on the canopy architecture and description. This study relies on the use of a coherent and full polarimetric scattering model, which simulates the backscattered fields by a forested area. In a first part, we study the frequential behavior of the interferometric phase center heights and in the second part, we focus on the attenuation coefficients. The behaviors of these two quantities are compared and in a third part, we propose to empirically derive a relation between these two quantities and the mean forest height. Finally, we investigate if a change of the incidence angle has an impact on the determination of this relation.

The interpretation of radar data would ideally require extensive and numerous observations. However, the number of observations is limited by the difficulty and the cost of acquiring ground truth and radar data. On the other hand, numerical models can provide a wide range of situations, both in inputs and in outputs. More precisely, they have to provide radiometric, polarimetric, and interferometric simulations and be applicable to various forested areas (high density, high/low moisture, inhomogeneous area, etc.) and radar configurations (low/high frequency, bistatic observation, etc.). This paper is dedicated to the presentation of the capabilities of a descriptive coherent scattering model (COSMO) applied to the electromagnetic study of the backscattering by forested areas. Improvements have been implemented in order to produce in output a radar image, which can be treated with the same polarimetric and interferometric tools as those applied to real synthetic aperture radar images. Thus, comparisons are possible. COSMO has been widely tested from P- to L- bands, over temperate and tropical forests and applied to radiometry, polarimetry, and interferometry. It appears finally as an efficient simulating tool to carry out parametric studies and to analyze how the total scattered field is built from canonical mechanisms and individual scatterer contributions.

The interpretation of SAR data remains particularly difficult in case of forests, which makes the use of modelling very helpful. On the other hand, the potential of combined use of interferometric and polarimetric data has been already demonstrated to provide key forest parameters. In this paper, we present comparisons between interferometric polarimetric images of a forest simulated by a coherent scattering model derived from L. Thirion (2003) and L. Thirion (2004), and real airborne and full polarimetric interferometric P-band data measured over maritime pine trees. Results are presented that demonstrate the efficiency of the model to retrieve polarimetric and interferometric parameters on this forest. Finally, a first step of simplification and inversion of the model, relying on the analysis of the scatterers contributions, is proposed

We propose to use a coherent model in order to compute the backscattering coefficient /spl sigma/ and the interferometric coherence /spl gamma/ of several stands of mangrove forests at P band. At this frequency, we may expect strong coherent effects reflecting the interactions between the ground and the forest scatterers. Simulation results are discussed: the different contributions are analyzed in order to quantify their impact on /spl sigma/ and /spl gamma/.