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The differences in sensor imaging mechanisms, observation angles, and scattering characteristics of terrestrial objects significantly limit the registration performance of synthetic aperture radar (SAR) and optical heterologous images. Traditional methods particularly struggle in weak feature regions, such as harbors and islands with substantial water coverage, as well as in desolate areas like deserts. This paper introduces a robust heterologous image registration technique based on region-adaptive keypoint selection that integrates image texture features, targeting two pivotal aspects: feature point extraction and matching point screening. Initially, a dual threshold criterion based on block region information entropy and variance products effectively identifies weak feature regions. Subsequently, it constructs feature descriptors to generate similarity maps, combining histogram parameter skewness with non-maximum suppression (NMS) to enhance matching point accuracy. Extensive experiments have been conducted on conventional SAR-optical datasets and typical SAR-optical images with different weak feature regions to assess the method's performance. The findings indicate that this method successfully removes outliers in weak feature regions and completes the registration task of SAR and optical images with weak feature regions. [ABSTRACT FROM AUTHOR]

The video synthetic aperture radar (ViSAR) system can utilize high-frame-rate scene motion target shadow information to achieve real-time monitoring of ground mobile targets. Modeling the characteristics of moving target shadows and analyzing shadow detection performance are of great theoretical and practical value for the optimization design and performance evaluation of ViSAR systems. Firstly, based on the formation mechanism and characteristics of video SAR moving target shadows, two types of shadow models based on critical size and shadow clutter ratio models are established. Secondly, for the analysis of moving target shadow detection performance in ViSAR systems, parameters such as the maximum detectable speed of moving targets, the minimum clutter backscatter coefficient, and the number of effective shadow pixels of moving targets are derived. Furthermore, the shadow characteristics of five typical airborne/spaceborne ViSAR systems are analyzed and compared. Finally, a set of simulation experiments on moving target shadow detection for the Hamas rocket launcher validates the correctness and effectiveness of the proposed models and methods. [ABSTRACT FROM AUTHOR]

When airborne synthetic aperture radar (SAR) encounters long-time global navigation satellite system (GNSS) denial, the system cannot eliminate inertial navigation system (INS) accumulated drift. Platform positioning technology based on SAR image-matching is one of the important auxiliary navigation methods. This paper proposes a three-step positioning method for an airborne SAR platform, which can achieve the robust and high-precision estimation of platform position and velocity. Firstly, the motion model of the airborne SAR platform is established and a nonlinear overdetermined equation set of SAR Range-Doppler based on the ground-control points set obtained by SAR image-matching is constructed. Then, to overcome the ill-conditioned problem generated by the singular Jacobian matrix when solving the equations directly, a three-step robust and high-precision estimation of platform position and velocity is achieved through singular value decomposition and equation decoupling. Furthermore, the error transfer model of systematic and random platform positioning errors is derived. Finally, a set of semi-physical simulation experiments of airborne SAR is conducted to verify the effectiveness of the positioning method and the accuracy of the error model presented in this paper. [ABSTRACT FROM AUTHOR]

Effective utilization of airborne synthetic-aperture (Airborne SAR) imagery often requires precise location of each image pixel. Historically, the positioning of airborne SAR imagery either relies on the use of reliable reference points to determine the relative position of the image, or requires the precise motion information of the aircraft and the characteristics of the SAR data collection system as input to determine the absolute position of the image. However, for many applications, the accuracy of traditional positioning methods is not high due to the challenge in obtaining the accurate geographic positions of reliable reference points and the inaccuracy of the recorded aircraft motion information. This study introduces an airborne SAR image planar positioning approach based on the premise that the systematic positioning error of the dual-view airborne SAR images are relatively consistent. The suggested planar positioning method applies the positioning auxiliary parameters of the initial ground-range airborne SAR image to ascertain the transformation relationship between the target's initial geographic position and pixel position, and it then uses the equivalent equation for the position of the homologue point to assess the systematic positioning error of the SAR image and determine the geographic position of a pixel in a digital SAR image. This approach has advantages over previous techniques in that it requires no precise geographic position information of the ground reference points, and on the basis of using the RD model to accomplish coarse positioning of four corners of SAR image, it no longer needs aircraft trajectory data. Tests were conducted using two airborne SAR images actually captured, and the experimental results indicate that the proposed method can achieve high precision planar positioning of dual-view airborne SAR images. Error sources are analyzed and recommendations are given to improve image positioning accuracy in future airborne SARs. [ABSTRACT FROM AUTHOR]

Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 × 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In point-based heterologous image matching algorithms, high-quality interest point detection directly affects the final image matching quality. In this paper, starting from the detection mechanism of each interest point detector, optical images and SAR images with different resolutions and covering different areas are selected as experimental data. The five state-of-the-art SAR-Harris, UND-Harris, Har-DoG, Harris-Laplace and DoG interest point detectors are analyzed in terms of scale difference adaptability, nonlinear intensity difference adaptability, distribution uniformity, image registration alignment performance and detection efficiency. Then, we performed registration experiments on images from different sensors, at different times, and at different resolutions to further validate our evaluation results. Finally, the applicable image types of each detector are summarized. The experimental results show that SAR-Harris has the best performance in scale difference adaptability, and UND-Harris has the weakest performance. In terms of nonlinear intensity difference adaptability, SAR-Harris and UND-Harris are comparable, and DoG performance is the weakest. The distribution uniformity of UND-Harris is significantly better than other detectors. Although Har-DoG is weaker than Har-Lap and DoG in repeatability, it is better than both in final image alignment performance. DoG is superior in detection efficiency, followed by SAR-Harris. A comprehensive evaluation and a large amount of experimental data are used to evaluate and summarize each detector in detail. This paper provides a useful guide for the selection of interest point detectors during heterologous image matching. [ABSTRACT FROM AUTHOR]

Airborne synthetic aperture radar (Airborne SAR) can accurately locate key targets and regions by using the flight parameters of the aircraft and the relative position information between the aircraft and the target which can be obtained from the airborne positioning and orientation system (POS). In the course of flight, the aircraft will deviate from the ideal flight path due to atmospheric turbulence, which results in the calculation deviating from the actual target position. In order to improve the target positioning accuracy, it is necessary to study the influence of aircraft motion error on the target positioning error. This study discusses the positioning accuracy of single-view airborne SAR from the perspective of linear Range-Doppler algorithm (RDA), and deduces the multi-view airborne SAR positioning error transfer model based on the multi-view airborne SAR positioning model. Based on these, we analyze the main factors that affect the positioning accuracy of the two positioning methods in detail and quantitatively reveal the mechanism by which the multi-view airborne SAR positioning method can improve the target positioning accuracy compared with the single-view airborne SAR positioning method; we also solve the problem of course planning for multi-view airborne SAR optimal positioning. The research results can provide theoretical support for the analysis of factors influencing positioning error and the positioning error correction of airborne SAR. [ABSTRACT FROM AUTHOR]

Video synthetic aperture radar (VideoSAR) can detect and identify a moving target based on its shadow. A slowly moving target has a shadow with distinct features, but it cannot be detected by state-of-the-art difference-based algorithms because of minor variations between adjacent frames. Furthermore, the detection boxes generated by difference-based algorithms often contain such defects as misalignments and fracture. In light of these problems, this study proposed a robust moving target detection (MTD) algorithm for objects on the ground by fusing the background frame detection results and the difference between frames over multiple intervals. We also discuss defects that occur in conventional MTD algorithms. The difference in background frame was introduced to overcome the shortcomings of difference-based algorithms and acquire the shadow regions of objects. This was fused with the multi-interval frame difference to simultaneously extract the moving target at different velocities while identifying false alarms. The results of experiments on empirically acquired VideoSAR data verified the performance of the proposed algorithm in terms of detecting a moving target on the ground based on its shadow. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) has been treated as a weather independent system for a long time. However, with the development of advanced SAR configurations, e.g., high resolution, bistatic, geosynchronous (GEO), the influence of tropospheric propagation error, which strongly depends on the weather, has begun to receive attention. In this paper, we focus on the effect of deterministic background tropospheric delay (BTD) during the image formation of GEO SAR. First, the decorrelation problems caused by the spatial variation and BTD are presented. Second, by combining with the SAR imaging geometry, the BTD error is decomposed as constant error, spatially variant error, and time variant error, the influences of which are analyzed under different circumstances. Third, an imaging method starting from the meteorological parameters and the GEO SAR systematic parameters is proposed to deal with the decorrelation problems. Finally, simulations with the dot-matrix targets are performed to validate the imaging method. [ABSTRACT FROM AUTHOR]

Gaofen-3 is a Chinese remote sensing satellite with multiple working modes, among which the scanning synthetic aperture radar (ScanSAR) mode is used for wide-swath imaging. synthetic aperture radar (SAR) interferometry in the ScanSAR mode provides the most rapid way to obtain a global digital elevation model (DEM), which can also be realized by Gaofen-3. Gaofen-3 ScanSAR interferometry works in the repeat-pass mode, and image pair non-synchronizations can influence its performance. Non-synchronizations can include differences of burst central times, satellite velocities, and burst durations. Therefore, it is necessary to analyze their influences and improve the interferometric coherence. Meanwhile, interferometric phase compensation and rapid DEM geolocation also need to be considered in interferometric processing. In this paper, interferometric coherence was analyzed in detail, followed by an iterative filtering method, which helped to improve the interferometric performance. Further, a phase compensation method for Gaofen-3 was proposed to compensate for the phase error caused by the unsynchronized azimuth time offset of image pair, and a closed-form solution of DEM geolocation with ground control point (GCP) information was derived. Application of our methods to a pair of Gaofen-3 interferometric images showed that these methods were able to process the images with good accuracy and efficiency. Notably, these analysis and processing methods can also be applied to other SAR satellites in the ScanSAR mode to obtain DEMs with high quality. [ABSTRACT FROM AUTHOR]

The application of SAR tomography (TomoSAR) on the urban infrastructure and other man-made buildings has gained increasing popularity with the development of modern high-resolution spaceborne satellites. Urban tomography focuses on the separation of the overlaid targets within one azimuth-range resolution cell, and on the reconstruction of their reflectivity profiles. In this work, we build on the existing methods of compressive sensing (CS) and generalized likelihood ratio test (GLRT), and develop a multiple scatterers detection method named CS-GLRT to automatically recognize the number of scatterers superimposed within a single pixel as well as to reconstruct the backscattered reflectivity profiles of the detected scatterers. The proposed CS-GLRT adopts a two-step strategy. In the first step, an L1-norm minimization is carried out to give a robust estimation of the candidate positions pixel by pixel with super-resolution. In the second step, a multiple hypothesis test is implemented in the GLRT to achieve model order selection, where the mapping matrix is constrained within the afore-selected columns, namely, within the candidate positions, and the parameters are estimated by least square (LS) method. Numerical experiments on simulated data were carried out, and the presented results show its capability of separating the closely located scatterers with a quasi-constant false alarm rate (QCFAR), as well as of obtaining an estimation accuracy approaching the Cramer–Rao Low Bound (CRLB). Experiments on real data of Spotlight TerraSAR-X show that CS-GLRT allows detecting single scatterers with high density, distinguishing a considerable number of double scatterers, and even detecting triple scatterers. The estimated results agree well with the ground truth and help interpret the true structure of the complex or buildings studied in the SAR images. It should be noted that this method is especially suitable for urban areas with very dense infrastructure and man-made buildings, and for datasets with tightly-controlled baseline distribution. [ABSTRACT FROM AUTHOR]

Spaceborne multistatic synthetic aperture radar (SAR) tomography (SMS-TomoSAR) systems take full advantage of the flexible configuration of multistatic SAR in the space, time, phase, and frequency dimensions, and simultaneously achieve high-precision height resolution and low-deformation measurement of three-dimensional ground scenes. SMS-TomoSAR currently poses a series of key issues to solve, such as baseline optimization, spatial transmission error estimation and compensation, and the choice of imaging algorithm, which directly affects the performance of height-dimensional imaging and surface deformation measurement. This paper explores the impact of baseline distribution on height-dimensional imaging performance for the baseline optimization issue, and proposes a feasible baseline optimization method. Firstly, the multi-base multi-pass baselines of an SMS-TomoSAR system are considered equivalent to a group of multi-pass baselines from monostatic SAR. Secondly, we establish the equivalent baselines as a symmetric-geometric model to characterize the non-uniform characteristic of baseline distribution. Through experimental simulation and model analysis, an approximately uniform baseline distribution is shown to have better SMS-TomoSAR imaging performance in the height direction. Further, a baseline design method under uniform-perturbation sampling with Gaussian distribution error is proposed. Finally, the imaging performance of different levels of perturbation is compared, and the maximum baseline perturbation allowed by the system is given. [ABSTRACT FROM AUTHOR]

Persistent scatterer interferometry (PSI) has the ability to acquire submeter-scale digital elevation model (DEM) and millimeter-scale deformation. A limitation to the application of PSI is that only single persistent scatterers (SPSs) are detected, and pixels with multiple dominant scatterers from different sources are discarded in PSI processing. Synthetic aperture radar (SAR) tomography is a promising technique capable of resolving layovers. In this paper, new approaches based on a novel two-tier network aimed at robust and efficient detection of persistent scatterers (PSs) are presented. The calibration of atmospheric phase screen (APS) and the detection of PSs can be jointly implemented in the novel two-tier network. A residue-to-signal ratio (RSR) estimator is proposed to evaluate whether the APS is effectively calibrated and to select reliable PSs with accurate estimation. In the first-tier network, a Delaunay triangulation network is constructed for APS calibration and SPS detection. RSR thresholding is used to adjust the first-tier network by discarding arcs and SPS candidates (SPSCs) with inaccurate estimation, yielding more than one main network in the first-tier network. After network adjustment, we attempt to establish reliable SPS arcs to connect the main isolated networks, and the expanded largest connected network is then formed with more manual structure information subtracted. Furthermore, rather than the weighted least square (WLS) estimator, a network decomposition WLS (ND-WLS) estimator is proposed to accelerate the retrieval of absolute parameters from the expanded largest connected network, which is quite useful for large network inversion. In the second-tier network, the remaining SPSs and all the double PSs (DPSs) are detected and estimated with reference to the expanded largest connected network. Compared with traditional two-tier network-based methods, more PSs can be robustly and efficiently detected by the proposed new approaches. Experiments on interferometric high resolution TerraSAR-X SAR images are given to demonstrate the merits of the new approaches. [ABSTRACT FROM AUTHOR]

Following the development of synthetic aperture radar (SAR), SAR images have become increasingly common. Many researchers have conducted large studies on geolocation models, but little work has been conducted on the available models for the geometric correction of SAR images of different terrain. To address the terrain issue, four different models were compared and are described in this paper: a rigorous range-doppler (RD) model, a rational polynomial coefficients (RPC) model, a revised polynomial (PM) model and an elevation derivation (EDM) model. The results of comparisons of the geolocation capabilities of the models show that a proper model for a SAR image of a specific terrain can be determined. A solution table was obtained to recommend a suitable model for users. Three TerraSAR-X images, two ALOS-PALSAR images and one Envisat-ASAR image were used for the experiment, including flat terrain and mountain terrain SAR images as well as two large area images. Geolocation accuracies of the models for different terrain SAR images were computed and analyzed. The comparisons of the models show that the RD model was accurate but was the least efficient; therefore, it is not the ideal model for real-time implementations. The RPC model is sufficiently accurate and efficient for the geometric correction of SAR images of flat terrain, whose precision is below 0.001 pixels. The EDM model is suitable for the geolocation of SAR images of mountainous terrain, and its precision can reach 0.007 pixels. Although the PM model does not produce results as precise as the other models, its efficiency is excellent and its potential should not be underestimated. With respect to the geometric correction of SAR images over large areas, the EDM model has higher accuracy under one pixel, whereas the RPC model consumes one third of the time of the EDM model.