Airborne Synthetic Aperture Radar (SAR) Tomography (TomoSAR) technology facilitates the extraction of three-dimensional (3D) information of target scatterers. However, phase screens induced by radar platform trajectory errors causes TomoSAR defocusing, which adversely affects the accuracy of the forest vertical structure retrieval. Additionally, the phase screens exhibit space-variant characteristics, which significantly degrade the calibration performance estimated by traditional phase gradient autofocus (PGA). This paper proposes an improved phase calibration method synthesizing unconstrained optimization model and weighted PGA (WPGA), which effectively address the above challenges. Firstly, the SAR data stack is segmented into multiple subareas by assuming that phase screens are space-invariant within each small area, which reduces complexity and improves computational efficiency. Secondly, a WPGA method synthesizing the scatterer heights derived from an unconstrained optimization model is proposed to estimate the phase screens. Simulation experiments are conducted to validate the effectiveness of the proposed phase calibration method. Furthermore, the full-polarization SAR data stacks acquired by the BorTomoSAR campaign are used for tomographic focusing analysis. Experimental results demonstrate that the proposed method accurately estimates the phase screens at the P, L, and S bands, providing a efficient solution for the forest vertical structure retrieval.

Researchers in the fields of ecological environment and remote sensing pay considerable attention to the estimation of forest canopy height with synthetic aperture radar (SAR). The interest is due to the ability of SAR to penetrate the forest canopy and its sensitivity to forest properties through backscattered intensity. Recent advances in deep learning (DL) present the possibility to derive canopy height maps from single high-resolution (HR) SAR images using neural networks. SAR2Canopy, an innovative framework, is proposed in this paper for canopy height estimation based, which incorporates sensor and scattering knowledge into the estimation. The integration framework allows for the merging of reconstruction trunk scattering features by an equivalent dihedral corner reflector (DCR) scattering model into the supervised tree height estimation process. The proposed method attempts a new approach combining the physical characteristics of SAR data with the nonlinear feature learning ability of DL, potentially extending to different DL algorithms. Experiments are conducted with airborne fully polarimetric P-band SAR data from two areas. Compared to the baseline models, including UNet, DeepLabV3_ResNet50, and FCN_ResNet50, the proposed SAR2Canopy integrated with the DCR model achieves an increase of up to 13.66% in the coefficient of determination (R2), while reducing the root mean squared error (RMSE) and mean absolute error (MAE) by as much as 14.59% and 20.69%, respectively.

The inversion of forest structural parameters contributes to evaluation biodiversity and ecosystem functions, as well as enabling sustainable forest management. In this study, we propose a novel method for extracting forest parameters adopting P-band polarimetric synthetic aperture radar (SAR) data. Firstly, we utilize the Freeman-Durden decomposition to extract double-bounce scattering information, including ground-scatterer scattering, scatterer-ground scattering. Then, the scattering amplitude of tree trunks based on the principles of coherent scattering modeling is calculated. Additionally, we introduce the finite cylinder scattering amplitude function and utilize the constraints of the allometric growth model of vegetation to invert forest parameters. The reliability and effectiveness of the proposed method are verified through measurement data, with an RMSE of 3.35m for the inversion results. This research provides a new approach for inverting forest parameters and has potential applications in forest monitoring and ecological studies

The accuracy of the polarimetric evaluation of low frequency polarimetric synthetic aperture radar (PolSAR) systems based on distributed targets is uncertain when the ionospheric information is unknown. Therefore, in this paper we investigate the potential of polarimetric evaluation of LT-1 images based on a set of calibrators arranged for L-band LT-1 PolSAR images. First, reasonable assumptions are made for common calibration models for LT-1 images. The ideal backscatter matrix of the set of calibrators is subsequently used in conjunction with the polarimetric distortion matrices (PDMs) to construct constraints for solving the PDMs. Finally, for possible ambiguous values of the solution, we use phase consistency as well as the Frobenius norm to remove the ambiguous values of the PDMs and obtain the final evaluated results. In the experimental part, we verify the feasibility of the method based on simulation data. Thus, the method contributes to accurate evaluation of LT-1 images.

Benefiting from the rapid development of deep learning, the field of synthetic aperture radar (SAR) ship detection has been promoted with renewed vigor. However, due to the unique characteristics of SAR ship detection, several challenges have been encountered in the process of fusing SAR ship detection. On the one hand, some special properties of SAR images, including low resolution, small targets, dense ship inshore arrangements, and background clutter noise, increase the difficulty of detection; on the other hand, satellite detection requires a high degree of real-time and lightweight, and most traditional models fail to satisfy the requirements in two areas. After considering the above issues, we combine the advantages of space-to-depth convolution (SPDConv) and inception depthwise convolution (InceptionDWConv) to design SPD-InceptionDWConv (SIDConv), which resolves the above problems with fewer parameters and operations. Moreover, based on SIDConv, we also design light SIDConv (LSIDConv) and efficient SIDConv (ESIDConv) to further reduce the model complexity and to accelerate the detection process. With the above module as the core and YOLOv5 as the base, we propose a lighter and faster detector named LFer-Net, which has only 0.8M parameters and 1.9G FLOPs. Extensive experimental results on the SSDD and HRSID datasets confirm that our model is not only superior to other models with 98.2 and 90.6 accuracy but also achieves detection speeds of 144 and 96 frames per second (FPS), respectively.

Polarimetry synthetic aperture radar (SAR) interferometry (PolInSAR) has been well-established for forest height estimation. However, employing mono-frequency SAR data for PolInSAR tree height inversion presents inherent limitations, posing challenges to ensure inversion accuracy. This article presents a novel method for the inversion of vegetation parameters using dual-frequency (DF) four-stage PolInSAR, aiming to address the limitations observed in mono-frequency inversion. By leveraging the differential penetration of vegetation across distinct frequency bands, this method facilitates the derivation of more precise volume-only coherence and ground phase information. Applying the DF four-stage PolInSAR method to a substantial dataset of simulation results identifies the optimal band combination as P- and L-band. Moreover, the band combination that yields the most significant enhancement in accuracy is determined to be L- and S-band. These simulation results inform the design of the DF full-polarization SAR system. Subsequently, airborne SAR data are acquired using this L- and S-band full-polarization airborne SAR system over the Saihanba Forest Farm in Hebei, China. Ground-based LiDAR measurements serve as reference values for the comparison of PolInSAR inversion results. The DF four-stage PolInSAR method has a 5.46% improvement in the inversion accuracy of airborne SAR data. Both simulation and airborne SAR data inversion outcomes demonstrate a significant enhancement in forest height inversion accuracy achieved through the DF four-stage PolInSAR method compared to the mono-frequency approach.

Multitemporal synthetic aperture radar interferometry (MT-InSAR), capable of detecting both surface deformation and elevation with high precision, is used for many applications in earth observation. Conventional synthetic aperture radar (SAR) missions with a single beam only detect deformation along the line of sight (LOS) and relative elevation due to the undetermined model of phase wrapping. In a multisatellite SAR mission, measurements from different SAR geometry improve the sensitivity of the detectable deformation, especially to the deformation along the north–south (N-S) direction. However, it is difficult to combine the measurements from varying viewing angles since the absolute phase cannot be reconstructed without a ground control point. In this article, a tri-beam SAR system is introduced to detect 3-D deformation and derive multiview 3-D surface model from a single spaceborne platform. The accuracy of the 3-D deformation from the tri-beam SAR is exploited with varying squint and incident angles to obtain the optimal parameters of the three beams. Then a multidimensional coherent scattering model is used to simulate the multitemporal SAR data with different viewing angles. Regarding the tri-beam SAR, potential applications in earth observation including 3-D deformation monitoring, geodetic stereo SAR, and multiview 3-D forest reconstruction are investigated subsequently. The results of this study indicate that the tri-beam SAR is able to measure 3-D deformation and reconstruct 3-D surface model without ground control point.

This paper establishes a low degrees-of-freedom allometric vegetation model based on the Biomass and allometry database (BAAD). It consists of a 5-bit species encoding scheme and a 5-parameter relationship derived from the BAAD data records. Combined with opensource fractal tree generation engine, it can produce realistic tree samples of a large variety of species. A coherent electromagnetic scattering calculation method is developed for the vegetation model where the generalized Rayleigh-Gans (GRG) approximation and the infinite cylinder approximation are used to calculate the scattering matrix of leaves and branches/trunk. Four-path multiple scattering mechanisms between vegetation and the ground are considered and attenuations through vegetation canopy are also considered. The scattering model is validated against numerical methods. In addition, an end-to-end simulation tool is developed. Optical image is used to extract individual trees with center positions and crown diameters. The rest parameters are generated using the derived allometry model. Virtual 3D scene with vegetation on digital elevation map (DEM) can be generated and SAR images can be simulated. Several case studies are carried out for both polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) and multi-temporal interferometric SAR (InSAR). One case of the Mount Fuji area is simulated and validated against ALOS-2 data and demonstrates an average scattering coefficient error of less than 3dB. Additional cases of Southwest China, Hainan Island and the Great Khingan Mountain demonstrate the feasibility of the proposed simulation scheme for various application scenarios.