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.

Multibaseline (MB) synthetic aperture radar inter-ferometry (InSAR) is capable of reconstructing steep terrain height profiles, and MB phase unwrapping (PU) is one of the most critical and challenging steps in its processing chain. Existing MB PU algorithms usually suffer from poor noise robustness or low computational efficiency. In this work, we propose a fast and robust MB PU algorithm, including three main steps: 1) construct the triangulation network guided by the quality map; 2) obtain robust estimation of ambiguity number gradients based on the intrinsic relationship between interferograms and gradient regularization; 3) solve minimum cost flow problem with modified weights. The mean absolute errors of the height profiles constructed by the proposed method are 1.6m and 1.3m for simulated data and real data experiments respectively, verifying the effectiveness of the proposed method.

For azimuth multi-channel (AMC) high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) systems, non-uniformly sampled signals will lead to false targets appearing in the image, which has a great impact on image quality and target detection. Many spectrum reconstruction methods for stationary scenes have been proposed to obtain images without false targets, but it is ineffective for moving targets due to the phase error caused by radial velocity. To present the radial velocity effect on the reconstructed image clearly, we establish a precise relation between the characteristics (imaging position, residual RCM, and amplitude) of false targets and radial velocity from the Doppler domain perspective. According to the effect analysis, we propose an image-domain false target suppression and radial velocity estimation method for moving targets. First, obtain multiple images through imaging preprocessing. Second, estimate and compensate the phase error of false targets based on the leastL1-norm optimization model to suppress false targets. Third, estimate the radial velocity of real moving targets based on the cross-correlation method. Compared to the existing methods, the proposed method is processed in the image domain which has a high signal-to-noise ratio (SNR) with the advantages of not requiring recognition and extraction of targets, lower computational complexity, and applicability for slow targets, fast targets, and multiple targets of the same range cell. The simulated SAR data and GaoFen-3 SAR data are processed to demonstrate the effectiveness of the proposed method. Furthermore, the radial velocity estimation method is verified by automatic identification system (AIS) information.

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.

The other dimension information of polarimetric synthetic aperture radar (SAR) provides the possibility for ground moving target indication (GMTI). The moving target detection method for traditional quadrature-polarimetric (Quad-Pol) has been proposed, and the effectiveness has also been verified. But this method is not suitable for the hybrid Quad-Pol SAR system. In this letter, we proposes a novel moving target detection method for hybrid SAR. First, transform the scattering matrix measured from hybrid Quad-Pol SAR to obtain the synthesized cross polarization (cross-pol) images, which has the azimuth ambiguity of odd-order copol channels. Then, according to the reciprocity theorem, clutter suppression can be implemented by combining the synthesized cross-pol images. What’s more, because the energy of copol channels is stronger than that of the cross-pol channels, the proposed method has better effect than the traditional method. Simulations demonstrate the effectiveness of the proposed method.

Ground moving target indication (GMTI) is one of the important applications of synthetic aperture radar (SAR). Due to the finite pulse sampling rate in azimuth, the azimuth ambiguity appears in spaceborne SAR systems, which not only reduces the image quality, but also affects the accuracy of moving target detection. The existing ambiguity suppression methods cannot retain moving targets which are overlapped with ambiguity, or cannot suppress the ambiguity in SAR-GMTI. In order to overcome the shortcomings of existing methods, this letter proposes an effective azimuth ambiguity suppression method based on local azimuth ambiguity-to-signal ratio (AASR) estimation and ambiguity focusing for SAR-GMTI. This method can effectively suppress the ambiguity, and is applicable to the situations where the moving target is overlapped with the ambiguity, large scenes and higher order ambiguity. Finally, the effectiveness of the proposed method is verified and demonstrated by the experimental results on GaoFen-3 SAR.

Compared with the rapid development of single-frequency polarimetric SAR (PolSAR) image classification technology, there is less research on the land cover classification of multi-frequency PolSAR (MF-PolSAR) images. And the deep learning methods among them are mainly based on convolutional neural networks (CNNs), only local spatiality is considered but the nonlocal relationship is ignored. Therefore, this paper proposes the MF semantics and topology fusion (MF-STF) model based on semantic interaction and nonlocal topological structure to improve MF-PolSAR classification performance. During MF-STF optimization, the semantic information-based classification (SIC) and topological property-based classification (TPC) work collaboratively, not only fully leveraging the complementarity of bands, but also combining local and nonlocal spatial information to improve the discrimination of different categories. For SIC, the designed cross-band interactive feature extraction (CIFE) module is embedded to explicitly model the deep semantic correlation among bands, thereby leveraging the complementarity of bands to make ground objects more separable. In TPC, the graph sample and aggregate network (GraphSAGE) is employed to dynamically capture the representation of nonlocal topological relations between land cover categories. In this way, the robustness of classification can be further improved by combining nonlocal spatial information. Finally, a MF weighted fusion (MFWF) strategy is proposed to merge inference from different bands, so as to make the MF joint classification decisions of SIC and TPC. Notably, its weights are adjusted based on the total model loss. The effectiveness of the proposed modules is proved by ablation experiments on three measured MF-PolSAR datasets. In addition, the comparative experiments show that MF-STF can achieve more competitive classification performance than some state-of-the-art methods.

In multichannel high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) ground moving target indication (GMTI) systems, Doppler centroid (DC) is an essential parameter for spectrum reconstruction and image focusing. However, the conventional DC estimator faces many problems in moving target with multichannel SAR system, such as nonuniform data in azimuth, subsignal usage, and channel mismatch. To estimate the DC of moving target, the modified cross-correlation coefficient (MCCC) method for multichannel SAR system is proposed in this letter, which is especially suitable for nonuniform and undersampled data. Simulations with real spaceborne data demonstrate the effectiveness of this method.

In azimuth multichannel high-resolution and wide-swath (AMC-HRWS) synthetic aperture radar (SAR) system, undersampling of azimuth signal results in the failure of the single-channel echo imaging, so the echoes of all the channels should be reconstructed to obtain the unambiguous SAR image. Many methods have been proposed to reconstruct the stationary scene echoes. However, moving targets (MTs) will cause false targets in the reconstructed SAR image, which has a great influence on SAR image interpretation and moving target detection, especially in maritime scenes. Hence, we propose an unambiguous imaging method of the moving target for maritime scenes with AMC-HRWS SAR. First, azimuth deramp processing is introduced to obtain coarse-focused moving targets, and then their echoes can be extracted from the sea clutter. Based on the extracted data, a radial velocity estimation method is proposed, which is high efficiency and does not require redundant channels. In addition, based on the estimated radial velocity and the characteristics of the coarse-focused signal, a novel signal reconstruction method of moving target is presented. The reconstruction performance of this method is not affected by channel imbalance, and false targets can be removed even if there is radial velocity error. Besides, the effectiveness of this scheme is verified by the simulation data and GaoFen-3 SAR real data. The estimated radial velocities of ships are verified by automatic identification system (AIS) information. The imaging results show that false targets are effectively suppressed, and moving targets are located in correct positions.

Dual-frequency polarimetric synthetic aperture radar (PolSAR) data can provide more information than single-frequency data, which can effectively improve classification accuracy. However, how to obtain sufficient and non-redundant feature representation from dual-frequency PolSAR data remains to be resolved. Besides, deep learning has shown good performance in PolSAR image classification, but it often requires a large number of labeled samples to participate in the training process, which is time-consuming and labor-intensive. In this paper, we propose a novel dual-frequency PolSAR image semi-supervised classification method that combines a dual-frequency joint feature learning (DFJFL) module with a cross label-information network (CLIN). First, the DFJFL module is developed based on the consistency and complementarity of dual-frequency data. It eliminates information redundancy by feature constraint loss function, and obtains compact dual-frequency joint feature representation. Subsequently, in order to avoid the influence of speckle noise, the proposed CLIN not only applies consistency regularization under network perturbation, but also uses the scattering mechanism of PolSAR data to find similar sample pairs to complete the consistency regularization under input perturbation, thereby achieving semi-supervised classification for PolSAR data. Experiments on four real dual-frequency PolSAR datasets verify that the proposed method can effectively extract dual-frequency PolSAR information, and make full use of unlabeled samples to improve classification accuracy. At the same time, compared with several related image classification algorithms, the proposed method could achieve the best performance.

Azimuth ambiguity degrades the quality of synthetic aperture radar (SAR) images and leads to the increase of false alarm rate in ground moving target indication (GMTI). Due to the existing azimuth ambiguity, suppression methods do not remove the first-order ambiguity completely and ignore the ambiguity above first order as well, moving target detection is affected by residual ambiguity. Hence, a novel method to suppress first-order and higher order azimuth ambiguities for the dual-channel SAR/GMTI is proposed in this letter. First, the displaced phase center antenna (DPCA) technique is applied to suppress clutter. Then, estimate the local azimuth ambiguity-to-signal ratio (LAASR) to find out the area affected by ambiguity. Finally, an inpainting algorithm is improved to patch the ambiguity area. The proposed method can remove the ambiguity almost completely. Moreover, the method is verified using the GaoFen-3 SAR dual-channel complex image data, and the result shows that the false alarm of moving target detection is degraded without reducing the detection rate.

Azimuth ambiguity greatly affects the image quality and application of synthetic aperture radar (SAR). Several azimuth ambiguity suppression methods have been proposed; however, these methods cannot remove the ambiguity completely and only the first-order ambiguity has been considered. Hence, in this letter, a novel method based on Wiener filtering and local azimuth ambiguity-to-signal ratio (AASR) estimation for N-order azimuth ambiguity suppression is proposed. The Wiener filtering is used to attenuate the ambiguity energy. Then the original image and the filtered image are combined to estimate local AASR, which is used to identify the ambiguity pixel. Finally, the ambiguity can be removed via interpolation. Through this method, the N-order ambiguity energy can also be suppressed to a lower level, and simultaneously, the consistency, resolution, and signal-to-noise ratio of an SAR image are maintained. Furthermore, in order to verify the practicability of the proposed method, it has been tested on the GaoFen-3 image and TerraSAR-X image.

A novel algorithm of strong clutter suppression for spaceborne dual-channel SAR/GMTI is proposed in this paper. First, the adaptive two-dimensional (2D) channel calibration is performed to calibrate difference between channels. Then, the amplitude correction is performed cell by cell to balance channels amplitude. At last, the refined phase correction based on the selected strong clutter is performed to correct phase error between channels. Through the process, channel error is corrected. Clutter, especially strong clutter, is well suppressed. The validity of the proposed algorithm is verified by GaoFen-3 SAR real data.

Clutter suppression is a key step for efficient detection of moving targets and accurate estimation of their parameters. Current clutter suppression approaches are available, in the case that clutter echoes of each channel are free of Doppler ambiguity. However, for multichannel high-resolution wide-swath (HRWS) synthetic aperture radar (SAR) system, the received echoes of each channel suffer Doppler ambiguity and thus current clutter suppression approaches may not perform well. To address this issue, the signal models of stationary and moving target with Doppler ambiguity are derived. By analyzing the signal models, this paper proposes a clutter suppression approach for multichannel SAR system with the capability of HRWS imaging. Simulated results and real data results both demonstrate the validity of the proposed approach.

Wide area surveillance (WAS) radar can monitor a large area repeatedly and acquires information of moving targets appeared in interesting area. In this paper, a novel algorithm of moving targets detection and parameters estimation is proposed for dual-channel WAS radar. The paper analyzes parameter ambiguous problem, and presents the corresponding solution. The effectiveness of the proposed algorithm is demonstrated by both the simulated data and real data.

The paper proposes a nonparametric method for ground moving-target indication (GMTI) using along-track interferometric synthetic aperture radar (SAR), which does not require the theoretical derivations of the joint probability density function of the interferometric magnitude and phase for both homogeneous and heterogeneous clutter. In the proposed approach, a best-fit curve is set as the detection threshold to separate moving targets from clutter and noise. A special technique, termed sliding-window technique, is then employed to reduce residual clutter and noise. The validity of the method has been demonstrated by the experiments performed on simulated spaceborne SAR data.

Provides a novel multi-channel SAR moving targets detection and image (MTDI) method. After clutter is cancelled by using three apertures, two ways of signals through which moving targets can be detected are gained. Then Doppler centroid and Doppler frequency rate can be estimated, so both the real positions and velocities of moving targets will be acquired. Finally some typical computer simulation results are presented which illustrate the method's validity.