The traditional 2-D multiplication modulation jamming (2D-MMJ) method is widely used in synthetic aperture radar (SAR) deception jamming due to its simple hardware implementation and convenient template preparation. However, it suffers from serious defocusing in high-resolution spaceborne SAR deception jamming. To improve the fidelity of deception jamming, this letter proposes an improved 2D-MMJ method based on defocus correction (2D-MMJDC). First, the defocus correction phase is designed by analyzing the range cell migration (RCM) curves between the real and jamming signals. Next, considering the false point-target azimuth deviation caused by the defocus correction, the azimuth deviation correction phase is designed. Finally, two phases are added to the 2D-MMJ method, and a template preparation process based on the 2-D fast Fourier transform (2D-FFT) is designed. Simulation results show that the 2D-MMJDC method effectively solves the problem of defocusing on the 2D-MMJ method and greatly improves the fidelity of the deception jamming.

The existing single-frequency tomographic synthetic aperture radar (TomoSAR) 3-D reconstruction method suffers from insufficient observation information due to the high cost and long period of acquisition of the required multi pass data. To effectively alleviate these problems, it is a feasible solution that a sensor carries dual-frequency radars that can work simultaneously and acquire dual-frequency data for 3-D imaging. This letter systematically introduces the theory and model of dual-frequency TomoSAR 3-D imaging system, and proposes a dual-frequency distributed compressive sensing (DF-DCS) TomoSAR method for reducing flight requirements. First, this method combines the neighborhood pixel information to effectively improve the signal-to-noise ratio (SNR). Then, the reflectivity profiles of the dual-frequency data are jointed to increase the unambiguous height and the accuracy of the target position estimation. Finally, the point-target simulation experiments and surface-target experiments based on SAR simulation (SARSIM) system verify the effectiveness of the proposed method. With the reconstruction accuracy maintained, the DF-DCS method can effectively improve the ability to resolve ambiguity and reduce the required flights.

The selection of statistically homogeneous pixels (SHPs) is essential for precise deformation monitoring in distributed scatterer synthetic aperture radar interferometry (DS-InSAR). Current SHP selection methods face challenges in test efficacy under small-sample and low-contrast conditions, resulting in imbalanced Type I and Type II errors and poor detection of weak heterogeneity. To address these issues, the wide-scope homogeneous testing (WSHT) algorithm is introduced, which enhances the hypothesis test of confidence interval (HTCI) by calculating the extremes of the confidence interval length and integrating Baumgartner-Weiss–Schindler (BWS) to minimize this length and improve the accuracy of the reference pixel mean. Simulations demonstrate that WSHT outperforms BWS and HTCI, achieving accuracy improvements of 71.00% and 34.71%, respectively. Analysis of Sentinel-1 images from Shenzhen City further highlights WSHT’s performance, achieving the highest SNR of 0.2755, a balanced speckle suppression index (SSI) of 5.5486, and the lowest mean squared error (MSE) of 0.2089, outperforming BWS and HTCI in noise suppression, resolution preservation, and robustness to sample size variations.

Multimodal image matching (MMIM) poses persistent challenges in remote sensing due to substantial variations in radiometric properties, scale, and rotation across different modalities. Traditional approaches typically employ pyramid scale space constructions to handle scale differences. However, the processing of multi-scale features often incurs a high computational cost and reduced efficiency. To address this issue, this paper proposes a novel MMIM algorithm based on log-polar sampling (LPS), which achieves both scale and rotation invariance while significantly reducing redundant computations. The proposed method begins with a feature detection strategy based on a single-scale edge thinning map (ETM), which is generated by combining Gaussian steerable filtering with an iterative edge thinning process to extract edge points accurately. To mitigate the impact of radiometric discrepancies between modalities, a quantized orientation map (QOM) is constructed using radiometrically invariant feature orientations. LPS is subsequently performed within neighborhoods of keypoints to generate logpolar patches (LPPs) that are inherently invariant to scale and rotation. These LPPs are then encoded into feature descriptors via orientation distribution histograms. A two-stage matching strategy is further introduced, incorporating keypoint elimination and descriptor reconstruction to enhance robustness. Extensive experiments conducted on six types of typical multimodal images demonstrate that the proposed algorithm consistently outperforms state-of-the-art MMIM methods in terms of both accuracy and computational efficiency. The source code and datasets will be publicly available at https://github.com/liujy325/LPSD.

Sea-surface velocity (SSV) and sea-surface height (SSH) are among the most crucial parameters in an oceanic dynamic environment. Using spaceborne interferometric synthetic aperture radar (InSAR) to obtain high-resolution, large-scale survey area, high observation frequency, and high-precision ocean dynamic parameters is advantageous. However, the hybrid baseline InSAR phase data include components from both SSV and SSH, making it challenging to distinguish them and affecting inversion accuracy without additional information. The PIE-1 constellation, the world’s first four-satellite distributed InSAR system, was successfully launched into orbit on March 30, 2023. This constellation can form multiple interferometric pairs by combining two satellites, enabling the potential to extract SSV and SSH from hybrid phase signals simultaneously. In this study, two pioneering and fundamental works were conducted: 1) an integrated current-height inversion model was developed based on the multichannel likelihood (ML) function, with error analysis performed according to PIE-1 parameters and 2) the detailed data processing scheme for the first simultaneous inversion of SSV and SSH based on spaceborne InSAR data was presented. The inversion results were compared to Doppler centroid analysis (DCA)-derived Doppler velocity and reference data from the ESA’s Copernicus Marine Service (CMEMS). Both qualitative and quantitative comparisons validated the effectiveness and accuracy of the inversion results. This approach represents an effective technique for simultaneous inversion of SSV and SSH in future multibaseline spaceborne/airborne InSAR systems.

Sensor errors, platform ephemeris errors, and auxiliary digital elevation model (DEM) errors can have an impact on the positioning accuracy of synthetic aperture radar (SAR) images. Utilizing corner reflectors for geometric calibration is a common way to improve parameter accuracy and therefore positioning accuracy. In this article, we propose an energy-based geometric self-calibration method without relying on corner reflectors. Based on the radiometric and geometric properties of SAR images, the energy of SAR orthophoto over the rugged mountainous areas is taken as objective function. The conjugate gradient method is used to estimate fast time offset and slow time offset by maximizing the objective function, which achieves the equivalent compensation for positioning errors. SAR images from Radarsat-2, COSMO-SkyMed, TerraSAR-X, GaoFen-3, LuTan-1 and ChaoHu-1 satellites were used for the experiments, and the experimental results demonstrate the effectiveness and applicability of the proposed method. The accuracy evaluation results of corner reflectors and field-measured checkpoints show that the proposed method improves the positioning errors of SAR images from different satellites from tens of meters to less than 10 m. Our proposed method not only provides a new perspective on the geometric calibration of SAR but also reduces the maintenance cost and the workload of external calibration during the daily operation of spaceborne SAR, which is of great significance for low-cost commercial satellites.

Discontinuous ground-based synthetic aperture radar monitoring (D-GBSAR) has gained increasing attention in the last five years, while the full processing framework has not been significantly reported. In this paper, a new full framework for D-GBSAR is presented, in which we integrate the advanced technique of image registration, permanent scatterer (PS) selection, phase filtering, repositioning error, and atmospheric phase compensation. Particularly, we reveal that the sensor’s spatial baseline is small thus making it relatively simple to registrate the images. Furthermore, we apply complex mean filtering to mitigate the stochastic noise for better performance. Thereafter, we utilize the newly proposed methods to perform the azimuth-based repositioning error and slant distance-based atmospheric phase compensation. Finally, typical experiments verify the effectiveness of the proposed method, which is comparable with the advanced method and showcases an important technical reference for D-GBSAR applications.

High-precision Digital Elevation Model (DEM) is crucial in geoscience, ecology, agriculture, hydrology, and other applications. In recent years, the rise of artificial intelligence algorithms such as machine learning has provided a new way to obtain high-precision DEM. However, machine learning algorithms rely on a large amount of training data, but there is currently a lack of open, unified, large-scale, and standardized multi-source DEM elevation error prediction data sets for large areas. Based on this, this paper proposes an open-source dataset, which is a large-scale elevation error prediction dataset based on TanDEM-X and AW3D30 DEMs in California, USA (T&Amp;A-DEM-California). The dataset consists of 10 feature attributes, contains 760,000 samples, and covers an area of 423,970,000 square kilometers. T&Amp;A-DEM-California not only provides academic researchers with benchmark datasets for the validation of new algorithms for machine learning but also provides a research base for future DEM calibration.

With the increasing number of remote sensing (RS) data sources, the joint utilization of multimodal data in Earth observation tasks has become a crucial research topic. As a typical representative of RS data, hyperspectral images (HSIs) provide accurate spectral information, while rich elevation information can be obtained from light detection and ranging (LiDAR) data. However, due to the significant differences in multimodal heterogeneous features, how to efficiently fuse HSI and LiDAR data remains one of the challenges faced by existing research. In addition, the edge contour information of images is not fully considered by existing methods, which can easily lead to performance bottlenecks. Thus, a joint classification network of HSI and LiDAR data based on Mamba (HLMamba) is proposed. Specifically, a gradient joint algorithm (GJA) is first performed on LiDAR data to obtain the edge contour data of the land distribution. Subsequently, a multimodal feature extraction module (MFEM) was proposed to capture the semantic features of HSI, LiDAR, and edge contour data. Then, to efficiently fuse multimodal features, a novel deep learning (DL) framework called Mamba, was introduced, and a multimodal Mamba fusion module (MMFM) was constructed. By efficiently modeling the long-distance dependencies of multimodal sequences, the MMFM can better explore the internal features of multimodal data and the interrelationships between modalities, thereby enhancing fusion performance. Finally, to validate the effectiveness of HLMamba, a series of experiments were conducted on three common HSI and LiDAR datasets. The results indicate that HLMamba has superior classification performance compared to other state-of-the-art DL methods. The source code of the proposed method will be available publicly athttps://github.com/Dilingliao/HLMamba.

Repositioning error (RE) compensation is one of the key steps in discontinuous ground-based synthetic aperture radar (D-GBSAR) monitoring. The traditional RE compensation is to perform 2-D phase unwrapping, and then remove the RE based on the least squares method, thereby introducing an extra unwrapping error. Specifically, due to the phase wrapped of the discrete permanent scatterers (PS), the least squares method is intractable to be performed directly. Hence, the core idea of this paper is to propose a new likelihood function model, and straightforwardly estimate the baseline parameters to compensate for the RE, avoiding the phase unwrapping. Firstly, we transform the discrete PS phase wrapped into a continuous function model, reducing the complexity of mathematical analysis. Then, based on the novel RE model in the context of Gaussian white noise, we obtain a concise mathematical expression of the Cramer-Rao lower bound (CRLB) for maximum likelihood estimation, which serves as the performance indicator for baseline estimation. Afterward, by introducing the Newton iteration method, we obtain the baseline estimation results and integrate a novel RE compensation deformation inversion processing methodology for D-GBSAR, named maximum likelihood-Newton iteration-RE compensation algorithm (MLNIRECA). Last but not least, the effectiveness of the proposed method is verified through simulation and real data experiments, where the root mean square error is constantly close to the CRLB with the increase of signal-to-noise ratio (SNR) when the SNR is greater than -10 dB. Particularly, we can extend the spatial baseline to 100 mm under the condition of accuracy requirements, and employ the proposed methodology to achieve sub-millimeter deformation monitoring accuracy over actual scenarios in time and space.

MIMO ground-based synthetic aperture radar (GB-SAR) have experienced rapid development in recent years because they can rapidly acquire echo data from observed scenes neglecting mechanical motion and thus can monitor the entire geological deformation process ranging from slow changes to rapid instabilities. However, the unique alternating transceiver configuration of MIMO GB-SAR systems presents rapidly varying periodic characteristics of the channel amplitude and phase errors, leading to severe paired false targets in the imaging results. Hence, their estimation and compensation accuracy requirements are extremely high due to the energy concentration properties of periodic amplitude and phase errors. In response to these concerns, this study develops a parameter model for the amplitude and phase errors of the transceiver channels and proposes an amplitude and phase errors parameter estimation method based on the minimizing of image entropy. The proposed algorithm involves imaging, pre-correction, and fine correction. Specifically, a non-interpolated sub-aperture imaging algorithm is proposed, then the amplitude and phase error parameter model is established, and pre-correction is performed using the model to reduce the number of iterations required for parameter estimation optimization. Finally, based on the parameter model, the amplitude and phase errors are estimated according to the entropy minimization criterion. Experimental results demonstrate that the proposed method can accurately estimates the channel phase cycle errors, significantly suppresses azimuth ambiguities, and achieves appealing focusing performance for the images.

This paper presents a simulation method for spaceborne SAR raw data. The method consists of geometric modeling, electromagnetic modeling and echo simulation. It can complete simulation tasks for different urban scenes, different satellites and different radar parameters. The effectiveness of the proposed method is verified by the simulation of point target and actual urban scene. In addition, we also design simulations for distributed SAR satellites and complete the 3D TomoSAR reconstruction experiment. The experiment results not only demonstrate the correctness of the simulation method from another perspective, but also show huge potential of the proposed method in supporting researches like SAR 3D reconstruction algorithm, satellite baseline design optimization and so on.

As a new deformation monitoring method, Discontinuous Ground-Based Synthetic Aperture Radar (D-GBSAR) monitoring has gradually attracted people’s attention, and the key problem it faces is how to compensate for the repositioning error caused by radar position offset. To address the above issue, this paper studies the geometric relationship between radar spatial baseline and target, and proposes a novel repositioning error compensation method based on trigonometric model and least squares parameter estimation. The feasibility of the proposed method is verified by the measured data of buildings monitoring, based on the permanent scatterer interferometry.

In order to compensate for the repositioning error introduced by the radar position offset in discontinuous GBSAR monitoring, a new mathematical framework for modeling the baseline error based on the Taylor expansion is developed in this letter. And then, a novel three-dimensional model called Multiparameter Nonlinear Trigonometric Model (MNTM) is proposed to accurately compensate for the repositioning error. Furthermore, to improve the compensating efficiency, we further develop an efficient two-dimensional model called Linear Trigonometric Model (LTM). Both simulation and field experiments verify the superiority and feasibility of the proposed methods, which measure the displacement with sub-millimeter accuracy.

The accurate interpretation of ocean waves in synthetic aperture radar (SAR) images is challenging because the scattering of moving waves causes various complex mechanisms. Moreover, SAR imaging is based on a unique nonlinear mapping relationship. This study proposed a full-link simulator to simulate the main parts of ocean surface backscattering, ocean surface motion, platform motion, echo generation and acquisition, SAR imaging, etc. The proposed simulator demonstrated the dynamic characteristics of the ocean surface by simulating the movement and decoherence effect of scattering cells within each radar pulse. Subsequently, a method for retrieving the wave direction spectrum from the SAR image spectrum based on the simulator was developed. The simulator, which can accurately reflect the modulation of backscattering, velocity bunching effect, and decoherence effect, was used as the forward transformation from the ocean wave spectrum to SAR image spectrum. In addition, the Levenberg-Marquardt iterative method was applied to determine the optimal solution through adjustment of several key parameters of the wave spectrum and ocean surface coherence time. Further, the Sentinel-1 SAR data were used to verify the accuracy of the full-link simulator and the effectiveness of the retrieval scheme. The results showed the following. 1) The SAR image with the simulation of both the moving scene and decoherence effect was highly consistent with the SAR image observed by Sentinel-1. 2) The retrieval scheme exhibited strong correction ability, thereby effectively improving the consistency between the retrieved and observed SAR spectra. In addition, the wave parameters (significant wave height, mean wave period, and dominant wave propagation direction) calculated considering the retrieved wave spectrum were consistent with the buoy measurements.