The atomic norm minimization (ANM) method has been well applied in tomographic SAR (TomoSAR) inversion, which can provide accurate scatterer localization and eliminates the outliers effectively. In order to solve the ANM, it is usually converted into a semidefinite programming (SDP) problem. However, this second-order optimization problem suffers from high computational cost when searching for the optimal solution. Since TomoSAR often faces large-scale processing of urban scenes, improving the computational efficiency will benefit greatly. In this paper, we develop and derive an efficient Alternating Direction Method of Multipliers (ADMM) implementation for the ANM method to solve the TomoSAR inversion problem, which is named as the ANM-ADMM algorithm. The detection performance, estimation accuracy, and computational efficiency of the proposed ANM-ADMM algorithm has been carefully analyzed by both simulation and real TerraSAR-X experiments. By comparing with the original ANM-SDP algorithm, it is clear that the ANM-ADMM algorithm can acquire considerable estimation accuracy and can meanwhile improve the computational efficiency significantly.

The interferometric vertical wavenumberkzhas a nonnegligible impact on the measurement accuracy. A critical study on the optimumkzfor forest height mapping must be carried out. This paper quantitatively investigates the PolInSAR inversion performance by the Cramér-Rao Lower Bound analysis. Through studying the relationship between the volume coherence and the inversion performance, a volume coherence condition for the existence of the optimumkzis first proposed for L-band PolInSAR inversion. The theoretical optimumkzcan then be easily obtained from the constant volume coherence level. We demonstrate that the established volume coherence condition can be useful for the system designers to optimize the system configurations before the PolInSAR mission.

The thermal protection system (TPS) ensures the flight safety of the high-speed spacecraft. However, different degrees of damage are unavoidable during the flight missions. In this paper, a nondestructive detection of on-orbit spacecraft TPS with ISAR imaging technology is proposed. The TPS with micro-damage structures such as cracks, debonding, warpage, holes etc. are modeled, the corresponding Ka- and W-band backscattered electric field data are simulated, and finally the ISAR two-dimensional images are generated with the back projection algorithm. The experimental results have well verified that ISAR imaging can effectively achieve high-precision images of the damaged thermal protection structures. Thus, through the real-time ISAR imaging of targets such as space stations from accompanying satellites, the possible damage types and damage locations can be monitored and displayed to achieve nondestructive detection of TPS and to evaluate the flight safety.

Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. Due to the sparsity of the elevation signal, the compressive sensing (CS) methods have been introduced for tomographic reconstruction. However, the limited irregular acquisitions and the dense sampling grids of the elevation cannot guarantee the sufficiently sparse reconstruction in the presence of noise. By constructing a complete set of atoms, the gridless sparse methods can directly recover the sparse signals in the continuous frequency space. In this paper, we propose the Atomic-norm minimization or the Gridless CS approach for tomographic SAR inversion and compare it with the L1-norm based optimization. The enhanced sparsity, the super-resolution capability and the more accurate estimates are demonstrated using the numerical simulations and experiments with real data. A Gridless CS reconstruction of an urban area of Shanghai from the TerraSAR-X data set are presented.

The interferometric vertical wavenumber (or baseline) has a direct impact on PolinSAR forest height mapping, which must be selected appropriately to acquire optimum inversion performance. In this paper, the key parameters influencing the height estimation precision are considered to simplify the system performance analysis. A PolinSAR performance optimization problem is then established according to the geometrical interpretation of the line coherence model. Finally, the contour map of optimum vertical wavenumber varying with forest height and wave extinction is intuitively provided, from which the system designers can easily determine the optimum baseline for PolinSAR forest height mapping.

Polarimetric synthetic aperture radar (SAR) data are inevitably contaminated by polarization crosstalk and channel imbalance, which propagate to the error of final remote sensing product. To ensure the successful estimation of forest heights from forthcoming polarimetric SAR interferometry (PolinSAR) campaigns, a critical study on the polarimetric system requirements of PolinSAR for forest height mapping must be carried out. This paper establishes an analytical model for forest height estimation error including dependences on polarimetric system parameters including crosstalk, channel imbalance, and system noise. Simulation analyses are conducted on the real airborne SAR data acquired by the E-SAR system to validate the proposed theoretical error dependence model. We demonstrate that the established error model can be used not only by the system designers as a guidance for setting the polarimetric system requirements of PolinSAR for forest height mapping, but also by the data analyst to correct for systematic bias in the forest height inversion.

Polarimetric interferometric SAR (PolinSAR) data are contaminated by cross-talk and channel imbalance. To ensure the successful estimation of forest heights from forthcoming PolinSAR campaigns, a critical study on the polarimetric system requirements of PolinSAR for forest height mapping must be carried out. In this paper, a triple-factor analysis of cross-talk, channel imbalance and noise of PolinSAR system is conducted to understand the polarimetric system requirements for PolinSAR forest height mapping. A model relationship between forest height estimation error and polarimetric system parameters is established through theoretical analysis. Meanwhile, the numerical relationship between the two is obtained by artificially adding different system errors to simulated SAR images. The experiment results well validate the correctness of our established model relationship. The polarimetric system requirements of PolinSAR for forest height mapping can be provided for system designers according to our established relationship.

Layover scatterers of tall building structures can be separated by synthetic aperture radar tomography (SAR-tomo). An iterative reweighted L1 minimization (IRL1) has been applied to enhance the sparsity in a tomographic inversion, where the basis pursuit (BP) technique was adopted to search for the solution. However, the IRL1 with BP is highly time-consuming, which may prevent its real application to large-scale data sets. In this letter, we propose the iterative reweighted alternating direction method of multipliers (IR-ADMM) for fast SAR-tomo imaging. We demonstrate and validate the enhanced sparsity and fast convergence of our IR-ADMM algorithm with experiments using both simulated data and TerraSAR-X Stripmap images of tall urban buildings. The experimental results show that compared with conventional IR-BP, the IR-ADMM greatly reduces the computation time without substantial performance degradation.

Multi-signal compressed sensing with total variation (MTV-CS) is developed for tomographic inversion of building structure. Incorporating with prior information of the building object, some particularly aligned pixels are combined via the minimization of the object function, as indicated by total variation regularization. A numerical simulation of scattering and SAR imaging of the buildings and the TerraSAR-X imaging data are applied for MTV-CS inversion.