P band synthetic aperture radar (SAR) tomography represents a powerful tool for characterizing the 3-D structure of tropical forests from their electromagnetic response. Current techniques separate the responses of the forest canopy and the underlying ground using SAR tomography, polarimetric diversity, and complex processing techniques. This letter shows that similar performance and better stability may be achieved using single-polarization data and parametric tomographic focusing, performed using a low-dimensional model. The vertical density of reflectivity of a tropical forest is modeled, at P band, using a Dirac function for the ground and a narrow peak for the volume. The performance of the proposed method is evaluated using P band tomographic data acquired during the TropiSAR campaign, and the results show that it can accurately and reliably estimate key structural parameters of the observed topical forest.

Interferometric phase linking (IPL) has become a prominent technique for processing images of areas containing distributed scatterers in SAR interferometry. Traditionally, IPL consists in estimating consistent phase differences between all pairs of SAR images in a time series from the sample covariance matrix (SCM) of pixel patches on a sliding window. This article reformulates this task as a covariance fitting problem; IPL appears then as a form of projection of an input covariance matrix so that it satisfies the phase closure property. This approach yields a systematic methodology to frame IPL as an optimization problem on the torus of phase-only complex vectors. On the modeling side, the formulation is modular and allows for a flexible choice of covariance matrix estimates, regularization options, and matrix distances. In particular, we demonstrate that most existing IPL algorithms appear as special instances of this framework. In addition, we propose some new options, which were not covered by the state of the art, whose merits are illustrated through simulations and a real-world case study. On the computational side, another contribution of this article is the derivation of generic and computationally efficient algorithms for IPL using majorization-minimization (MM) and Riemannian optimization.

Synthetic Aperture Radar (SAR) tomography has been successfully applied to the characterization of forest using 3D imaging. Nevertheless, the information content extracted by this technique is limited by the resolution in range and elevation. Also, it is demonstrated that a small number of parameters allows the reconstruction of tomograms that are close to the measured ones. In this paper, forest tomograms are reconstructed by an inversion method using forest scattering models with few parameters. Key forest parameters, such as ground position zg, forest height hvand the ratio between ground intensity and volume are determined using mono-polarized P-band data. These results are compared for different types of forest scattering models with verification data from the TropiSAR and TomoSense campaigns.

This paper proposes an algorithm for phase differences estimation in multi-temporal InSAR. The proposed approach is based on covariance fitting estimation and the majorization-minimization algorithm. Experiments with Sentinel-1 images of Mexico City demonstrate that the proposed approach compares favorably to the state-of-the-art phase linking (i.e., maximum likelihood-based approaches) when the sample support is low (i.e., when the number of pixels in the multi-look window cannot scale with the number of SAR images). Hence, the proposed approach can improve the spatial resolution of phase difference estimation in case of large SAR image time series.

Phase linking is a prominent methodology to estimate coherence and phase difference in interferometric synthetic-aperture radar. This method is driven by a maximum likelihood estimation approach, which allows to fully exploit all the possible interferograms from a time series. Its performance is, however, known to be affected by the accuracy of the covariance matrix estimation step, which usually requires to introduce additional prior information on its structure when there is a small sample support (spatial window). Moreover, most phase linking algorithms are built upon the sample covariance matrix, due to the assumption of an underlying Gaussian distribution. In a scenario where SAR data is high resolution, or when the study area is spatially heterogeneous (e.g., urban area), this assumption can also limit the accuracy of the covariance matrix estimation step. Considering the two aforementioned issues, we introduce alternative statistical models, whose maximum likelihood estimators then yield new phase linking algorithms. In order to be robust to non-Gaussian data, we consider the use of a more general model of scaled mixture of Gaussian. To address small sample support issues, we also generalize this approach to a possibly low-rank structured covariance matrix. A unified algorithm to perform phase linking given these models is then derived and validated by simulations and a real data case (Sentinel-1 data).

Remote sensing data are usually collected by SAR instruments on-board airplanes or satellites and many applications have been developed in a wide range of scientific subjects. Developments in technologies permit radar sensors to be miniaturized, which now allow them to be embedded on-board smaller platforms such as UAVs. ONERA is currently developing a new line of SAR instruments on-board UAV, called SAR-Light. In its first version, it is a mono-polarized (VV) SAR operating in X-band at high spatial resolution (0.25 m). This paper presents the SAR-Light instrument and describes the performance obtained during a flight tests campaign carried out in April 2021 in the South of France.

This paper presents a new algorithm for improving the estimation of interferometric SAR (InSAR) phases in the context of time series and phase linking approach. Based on maximum likelihood estimator of a multivariate Gaussian model, the estimation of the InSAR phases is solved using a Block Coordinate Descent algorithm. Compared to the state-of-the-art approaches, the main improvement lies on the joint estimation of the covariance matrix and the InSAR phases instead of using a plug-in coherence estimate obtained from the sample covariance of the data or the modeling of the temporal decorrelation of the target under observation. Results of synthetic simulations confirm the improvement brought by the proposed estimator.

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 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.