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.

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.

Global forest height and biomass mapping is an important issue for ecosystem studies. Polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) is an attractive technique for extracting forest parameters by inverting the physical scattering model. However, the simplified scattering model restricts the inversion accuracy unless multi-baseline measurements are adopted, and this will lead to increasing cost. In this paper, a complex-valued convolutional neural network (CV-CNN) model using PolInSAR features of single baseline is proposed for forest height mapping with high accuracy. The supervised learning process shows that the obtained CV-CNN model is accurate enough to describe the complicated forest scattering process. Both simulations and experiments on airborne E-SAR data set demonstrate that the proposed CV-CNN model-based method can greatly improve the forest height inversion accuracy. Experimental results show that the coefficient of determination (r2) increases from 0.83 to 0.92, while root-mean-square error (RMSE) decreases from 3.74m to 2.58m. This promising approach makes it possible to map forest heights more accurately from the single-baseline PolInSAR observations, which will further promote the wide use of PolInSAR technique.

The advancements in ship detection technology using convolutional neural networks (CNNs) regarding synthetic aperture radar (SAR) images have been significant. Yet, there are still some limitations in the existing detection algorithms. First, the backbones cannot generate high-quality multiscale feature maps. Second, there is a lack of suitable attention mechanisms to suppress false alarms. Third, the current feature intensification algorithms are unable to effectively enhance the shallow feature’s semantic information, which hinders the detection of small ships. Fourth, top-level feature maps have rich semantic information; however, as a result of the reduction of channels, the semantic information is weakened. These four problems lead to poor performance in SAR ship detection and recognition. To address the mentioned issues, we put forward a new approach that has the following characteristics. First, we use Convnext as the backbone to generate high-quality multiscale feature maps. Second, to suppress false alarms, the multi-pooling channel attention (MPCA) is designed to generate a corresponding weight for each channel, suppressing redundant feature maps, and further optimizing the feature maps generated by Convnext. Third, a feature intensification pyramid network (FIPN) is specifically designed to intensify the feature maps, especially the shallow feature maps. Fourth, a top-level feature intensification (TLFI) is also proposed to compensate for semantic information loss within the top-level feature maps by utilizing semantic information from different spaces. The experimental dataset employed is the SAR Ship Detection Dataset (SSDD), and the experimental findings display that our approach exhibits superiority compared to other advanced approaches. The overall Average Precision (AP) reaches up to 95.6% on the SSDD, which improves the accuracy by at least 1.7% compared to the current excellent methods.

The atomic norm minimization (ANM) method has been well-applied in tomographic synthetic aperture radar (TomoSAR) inversion, which can provide accurate scatterer localization and eliminate 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 article, 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 have 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.