When dealing with forest scenario, target scattering separation using synthetic aperture radar (SAR) tomography is a challenging task for the application of biophysical parameter retrieval approaches. One important and widely popular solution used to investigate the scattering mechanism separation based on multipolarimetric multibaseline (MPMB) SAR data is the sum of Kronecker products (SKPs), which provides the basis for decomposition of the data into ground-only and canopy-only contributions. In this paper, we investigate the possibility of characterizing multiple scattering mechanisms using the SKPs of covariance matrix. In particular, we present a method for characterization of forest structure using MPMB data that adapt SKP with the generalized volume description and the physical model of interferometric cross correlation as the sum of scattering contributions. According to the Freeman-Durden model, the method expresses the estimated covariance matrix in terms of the Kronecker product of polarimetric and interferometric coherence matrices corresponding to direct, double-bounce, and random-volume scattering mechanisms. The proposed method is tested with simulated and P-band MB data acquired by ONERA over a tropical forest in French Guiana in the frame of the European Space Agency's campaign TROPISAR. Comparison of the retrieved height of trees with a LiDAR-based canopy model as a reference showed that the proposed method has the potential to decrease root-mean-square error of forest height by up to 3.9 m with respect to SKP.

This paper deals with the compensation of phase miscalibration in the general context of tomographic synthetic aperture radar image focusing. Phase errors are typically independent of one acquisition to the other, thus leading to a spreading and defocusing in the multidimensional (3-D, 4-D, and 5-D) imaging space. Coping with this problem in presence of volumetric scattering is generally a complex issue. In this paper, we consider the approach for phase calibration characterized by the advantage, with respect to classical phase calibration algorithms, of not requiring either the identification of a reference target or specific assumptions about the unknown phase function, or a priori information about the terrain topography. The novelty of the proposed phase miscalibration estimation and compensation method is related to its ability to avoid unwanted and uncontrollable vertical shifts in the focused image. The estimation of the calibration phase is performed by optimizing the contrast or the entropy of the vertical profile with the constraint of a zero phase derivative. Such a constraint preserves the output height distribution. Experimental results of simulated and real data are included to demonstrate the effectiveness of the proposed method.

Height reconstruction in urban areas using multibaseline synthetic aperture radar (SAR) images is still a challenging task. Due to the superimposition of scatterers from different elevations in a same resolution cell, the generation of digital elevation model is not straightforward. Classical SAR interferometry cannot be adopted, while standard SAR tomography (TomoSAR) can fail to well identify and separate the scatterers in case that the relative contribution of a scatterer to the others is insignificant. Moreover, irregular and low number of sampling can increase the ambiguity level and introduce artifacts with strong variance from pixel to pixel. Coping with this problem typically requires large number of datasets with proper sampling. In this paper, contextual information is exploited to reduce the ambiguity and resolve the superimposition of the scatterers regardless of their relative power contributions, even in the case of a limited number of images. In particular, the proposed approach, starting from standard spectral estimators, introduces a regularization term to include the a priori information about scene height variation in the array processing chain. The reconstruction problem is set as an energy minimization problem that is solved using graph-cut-based optimization algorithms, where the solution of localization is the linkage between optimization of signal energy along direction of arrival and controlling height variation within the neighbors of selected pixel. Details of experimental results in the form of tomographic slices as well as three-dimensional point cloud generation, from simulated and real datasets are included to demonstrate the effectiveness of the proposed reconstruction approach.

Forest height and underlying terrain reconstruction is one of the main aims in dealing with forested areas. Theoretically, Synthetic Aperture Radar Tomography (TomoSAR) offers the possibility to solve the layover problem, making it possible to estimate the elevation of scatters located in the same resolution cell. This paper describes a deep learning approach, named Tomographic SAR Neural Network (TSNN), that aims at reconstructing forest and ground height using multipolarimetric multibaseline (MPMB) SAR data and Light Detection and Ranging (LiDAR) based data. The reconstruction of the forest and ground height is formulated as a classification problem, in which TSNN, a feed-forward network, is trained using covariance matrix elements as input vectors and quantized LiDAR-based data as the reference. In our work, TSNN is trained and tested with P-band MPMB data acquired by ONERA over Paracou region of French Guiana in the frame of the European Space Agency’s campaign TROPISAR and LiDAR-based data provided by the French Agricultural Research Center. The novelty of the proposed TSNN is related to its ability to estimate height with a high agreement with LiDAR-based measurement and actual height with no requirement for phase calibration. Experimental results of different covariance window sizes are included to demonstrate TSNN conducts height measurement with high spatial resolution and vertical accuracy outperforming the other two TomoSAR methods. Moreover, the conducted experiments on the effects of phase errors in different ranges show that TSNN has a good tolerance for small errors and is still able to precisely reconstruct forest heights.

This letter analyzes the impact of the accuracy of estimation of the data covariance matrix in synthetic aperture radar tomography of vegetated areas. The characterization of volumetric areas requires a robust estimation of the covariance matrix, which is usually performed by means of an averaging operation of neighboring pixels. In this letter, a different approach, based on the use of local and nonlocal (NL) neighborhoods of pixels, is evaluated. The analysis considers the quality of the reconstructed vertical profile obtained using both single and fully polarimetric multibaseline (MB) data. In the case of fully polarimetric data, two procedures for obtaining the vertical reconstruction are analyzed: the sum of Kronecker product decomposition of the covariance matrix and a procedure based on the full-rank estimation of a 3-D coherence matrix. The analysis of the impact of NL technique on the robust estimation of covariance matrix and on the capability of separating the interfering signals is performed using simulated and P-band real MB data.