Due to the influence of speckle noise and geometric distortion between images, synthetic aperture radar (SAR) image registration under different imaging conditions is a challenging task in remote sensing. To address the issues of significant differences in scattering and geometric characteristics of SAR images under different viewing angles, this letter proposes a novel SAR image registration method. The existing methods mainly rely on gradient information in the feature point selection process, which leads to uneven distribution of feature points and poor global matching. We design a Harris-based neighborhood difference map (HNDM) detector. This detector uses the degree of difference between neighbor regions and the central region to obtain feature points that are homogeneous and significant. Then, a Gaussian–Gamma-shaped (GGS) feature map is used to construct the feature point characterization, which is more robust to dark region noise. Experimental results of SAR image registration under different conditions show that our method achieves better performance in matching accuracy and the number of correct correspondences, outperforming three existing advanced algorithms.

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.

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.

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.

Due to the geometric and nonlinear radiometric differences (NRD) between SAR and optical images, coupled with the impact of speckle noise, achieving robust and high-precision matching between them remains challenging. This paper introduces a novel multi-scale SAR-optical image matching method, which employs an improved multi-scale matching strategy. Specifically, we first establish an image pyramid and detect geometric keypoints of the images, subsequently segmenting the images into sub-blocks centered around these keypoints. These sub-blocks are then fed into the proposed siamese feature description network, comprising of a structure-enhanced convolutional layer and a Transformer-CNN model, for feature descriptors extraction, followed by feature matching. Finally, the matching point pairs are obtained by combining the matching relationships between image pairs of different scales. Experimental results show that our proposed algorithm achieves better matching performance compared to the existing state-of-the-art algorithms.

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.

The SAR-optical image matching is a research hotspot in the field of remote sensing. In this letter, we propose an end-to-end learning based SAR-optical image matching algorithm. Initially, the algorithm applies local normalization filter on multi-model image pairs, and then detects keypoints with high repeatability and ease of matching via a self-supervised keypoint detection network. The detected keypoints are fed into our proposed Transformer-CNN dual branch feature description siamese network, extracting global and local contextual information of images to gain robust feature descriptors. In the training phase, we adopt a two-stage training strategy in the form of description then detection, which enables the keypoint detection network to learn in conjunction with the output of the feature description network, so as to obtain more robust keypoints. Experimental results show that our method achieves the average Root Mean Square Error (aRMSE) of 3.37 and 2.98 pixels on the test data, outperforming the three compared existing advanced algorithms.

The sliding spotlight synthetic aperture radar (SAR) system, as a high-resolution imaging mode, has played a crucial role in vessel detection and tracking over the ocean. Radial velocity is an important indicator for SAR vessel information extraction, which is of great significance for vessel positioning, actual velocity estimation, and target tracking. However, the unique operating mode and complex signal model of sliding spotlight SAR result in a complex coupling between Doppler frequency shift and azimuth offset caused by vessel motion, making traditional Stripmap mode radial velocity estimation methods unsuitable. This letter based on the imagery signal model of moving targets in sliding spotlight SAR mode, establishes the mapping relationship between radial velocity and theoretical Doppler shift. Finally, a vessel radial velocity estimation method for sliding spotlight SAR mode was proposed, combining the single-look complex (SLC) data from real SAR imagery. The proposed method is validated by simulation results and real SAR SLC data of GaoFen-3 (GF-3) sliding spotlight mode. Additionally, the processed results demonstrate that the estimation error has a root-mean-square (rms) value of 0.34 m/s when compared with the information provided by automatic identification system (AIS) data.

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.

Synthetic Aperture Radar (SAR) target image acquisition presents challenges and incurs high annotation costs. The emergence of self-supervised contrastive learning shows promise for SAR automatic target recognition (ATR) with limited data. However, SAR images suffer from poor discriminability and high sample similarity, hindering instance discrimination in contrastive learning. To address this, we propose Low-confidence Discriminant Contrastive Learning (LDCL), which integrates group-instance contrast and batch mixed training for SAR ATR. LDCL consists of two branches: classical instance discrimination and group-instance discrimination. We refine the SAR-group instance discrimination loss function by incorporating distance calculations to guide feature vectors towards nearest clusters, enhancing discrimination within the feature space. Additionally, we introduce a batch image mixing training strategy to reduce confidence in SAR instance discrimination while preserving intra-class consistency. Experimental results on small sample MSTAR and FUSAR-Ship datasets demonstrate that LDCL outperforms traditional transfer learning and self-supervised learning methods, achieving significantly higher recognition rates in SAR ATR tasks.

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.

Sea clutter and inherent speckle noise in synthetic aperture radar (SAR) images can pose challenges to accurate sea surface target detection, especially for phenomena such as ship wakes reliant on efficient feature extraction. Traditional denoising methods require manual tradeoffs between denoising effects and detail retention. Supervised denoising methods based on deep learning demand a substantial number of real noisy-clean image pairs for training, coupled with specific parameter settings and labeled data amounts. In response to these challenges, this article introduces Wake2Wake, a self-supervised denoising method aimed at enhancing the performance of existing deep learning-based ship wake detectors. The method incorporates a novel ship wake awareness (SWA) block designated to address the distinctive features of turbulent and Kelvin wakes. Furthermore, to overcome the source imbalance problem in the dataset, simulated wake data are integrated into the training process. This not only mitigates dataset imbalances but also significantly improves both denoising and detection performance. The experimental results indicate that Wake2Wake improves the accuracy of Rotated RepPoints by 3.6 mAP and S2A-Net by 2.6 mAP on the OpenSARWake dataset, respectively. The proposed approach achieves varied extents of improvement, showcasing its potential in mitigating sea clutter and enhancing feature extraction, especially in detecting SAR ship wakes.

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.