Airborne Synthetic Aperture Radar Research Articles

Mitigating Incidence Angle Effects in Airborne SAR Time-Series Crop Classification: Integrating Transfer Learning and Variational Mode Decomposition

Mitigating Incidence Angle Effects in Airborne SAR Time-Series Crop Classification: Integrating Transfer Learning and Variational Mode Decomposition

A Novel Method for Airborne SAR Tomography Baseline Error Correction Driven by Small Baseline Interferometric Phase

A Novel Method for Airborne SAR Tomography Baseline Error Correction Driven by Small Baseline Interferometric Phase

Highly Integrated Low-Profile Multilayer Dual-Polarized Phased Array Antenna With Truncated Cavities for First Pulsed Bistatic L-Band Airborne SAR Sensor

Highly Integrated Low-Profile Multilayer Dual-Polarized Phased Array Antenna With Truncated Cavities for First Pulsed Bistatic L-Band Airborne SAR Sensor

A Novel Airborne SAR MOCO Algorithm Based on Optimal Weighting Doppler Frequency Modulation Rate Estimation

A Novel Airborne SAR MOCO Algorithm Based on Optimal Weighting Doppler Frequency Modulation Rate Estimation

Classification evaluation and improvement of airborne PolSAR images for land use mapping using deep learning

Polarimetric synthetic aperture radar (PolSAR) images have been widely used in many fields due to its advantage in obtaining full polarization information, especially in land use classification. To evaluate the performance of airborne PolSAR images in land use classification, this paper systematically evaluated the potential value of PolSAR images in land use classification by using machine learning and deep learning algorithms, and improved the classification performance of airborne PolSAR images by constructing a multi-structural feature aware attention network (MSFA-Net). The classification performance of different algorithms was evaluated by the overall accuracy. The results showed that the machine learning algorithm Random Forest (RF) produced the lowest overall accuracy of 71.93%. Using RF as the baseline accuracy, the deep learning-based classification algorithm significantly improved the classification accuracy. Specifically, Wide Contextual Residual Network achieved an overall accuracy improvement of 8.87% to 80.80%, and Two-branch CNN generated an overall accuracy of 84.49%. Multi-source CNN produced the second highest overall accuracy of 87.25%, an improvement of 15.32%. The proposed MSFA-Net algorithm achieved the highest classification accuracy of 89.29% with an improvement of 17.36%, which further improves the land use mapping capability of airborne PolSAR images. A moderate reduction in resolution helps to improve classification accuracy, with 84.36% accuracy after 4-fold downsampling compared with the original resolution accuracy of 69.30%. On the other hand, noise, polarization error and motion error had a negative impact on the classification performance. This paper highlights the application of airborne SAR images in land use classification, and strengthens the understanding of deep learning and the influence of different factors on PolSAR images in land use classification.

Studies on High-Resolution Airborne Synthetic Aperture Radar Image Formation with Pseudo-Random Agility of Interpulse Waveform Parameters

By means of alteration of the transmitted linear frequency modulation (LFM) signal waveform parameters, such as pulse width or chirp rate, initial phase, pulse repetition interval (PRI), and chirp rate polarity at every position of synthetic apertures, the pseudo-random agility technology of interpulse waveform parameters in airborne Synthetic Aperture Radar (SAR) actively increases the complexity and uncertainty of radar waveforms. This technology confuses jamming interception receivers, thus improving its anti-interference ability for active coherent jamming, which is one of the main research interests of airborne SAR technology. But the pseudo-random agility technology for interpulse waveform parameters faces certain challenges of large computation and complex system design, which need to be further studied and solved. To address these issues, a processing scheme of high-resolution SAR image formation which is appropriate for agile interpulse waveform parameters is proposed in this paper. This method can deal with multiple agile parameters, not only single ones as in most existing literature. Its computation load is nearly comparable to that of traditional SAR image formation with constant waveform parameters. The high-resolution SAR imaging results obtained by processing SAR raw data with agile interpulse waveform parameters demonstrate the effectiveness of the proposed method. In addition, real SAR images with resolutions of 0.5 m and 0.15 m, which are rarely found in the public literature, are shown under the circumstance of randomly changing the transmitted wideband LFM signal pulse parameters one by one.

Optimized SAR imaging algorithm with SSL constraints for the Azimuth direction

In order to address the low azimuth imaging problem of traditional Range Doppler (RD) algorithm, a high-resolution imaging algorithm with optimal azimuth sampling sequence length (SSL) constraints is hereby proposed. According to this algorithm, efforts are made to design a formula for the calculation of the initial azimuth SSL on the basis of SAR azimuth imaging parameters. Having obtained the initial SSL and the criteria for the evaluation of SAR imaging performance, we continue to figure out the extreme values of the curve within a reasonable range of variation. On the basis of these values, the optimal azimuth SSL can be determined to provide data support for global azimuth imaging. Measurement data from imaging experiments on airborne and spaceborne SARs show that, compared with traditional RD algorithm, the proposed algorithm reveals outstanding advantages as indicated by a number of factors critical for the evaluation of imaging performance, such as resolution, peak sidelobe ratio (PSLR) and integral sidelobe ratio (ISLR). Compared with reference methods with local optimal imaging performance, the proposed algorithm can help to obtain global optimal SAR imaging data.

Above Ground Level Estimation of Airborne Synthetic Aperture Radar Altimeter by a Fully Supervised Altimetry Enhancement Network

Due to the lack of accurate labels for the airborne synthetic aperture radar altimeter (SARAL), the use of deep learning methods is limited for estimating the above ground level (AGL) of complicated landforms. In addition, the inherent additive and speckle noise definitely influences the intended delay/Doppler map (DDM); accurate AGL estimation becomes more challenging when using the feature extraction approach. In this paper, a generalized AGL estimation algorithm is proposed, based on a fully supervised altimetry enhancement network (FuSAE-net), where accurate labels are generated by a novel semi-analytical model. In such a case, there is no need to have a fully analytical DDM model, and accurate labels are achieved without additive noises and speckles. Therefore, deep learning supervision is easy and accurate. Next, to further decrease the computational complexity for various landforms on the airborne platform, the network architecture is designed in a lightweight manner. Knowledge distillation has proven to be an effective and intuitive lightweight paradigm. To significantly improve the performance of the compact student network, both the encoder and decoder of the teacher network are utilized during knowledge distillation under the supervision of labels. In the experiments, airborne raw radar altimeter data were applied to examine the performance of the proposed algorithm. Comparisons with conventional methods in terms of both qualitative and quantitative aspects demonstrate the superiority of the proposed algorithm.

Airborne SAR to Optical Image Registration Based on SAR Georeferencing and Deep Learning Approach

Airborne SAR to Optical Image Registration Based on SAR Georeferencing and Deep Learning Approach

MuA-SAR Fast Imaging Based on UCFFBP Algorithm with Multi-Level Regional Attention Strategy

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm.

On evaluating the efficacy of air-borne synthetic aperture radar for detecting polar bears: A pilot study

On evaluating the efficacy of air-borne synthetic aperture radar for detecting polar bears: A pilot study

Research on simulation method for nonlinear effects of airborne SAR electromagnetic interference

In order to simulate the nonlinear interference effect of high-power electromagnetic signals on airborne Synthetic Aperture Radar (SAR) imaging, the walk–stop geometric modeling of airborne SAR was established, and the range time-domain pulse coherence method was used to simulate SAR echo. By establishing a SAR receiver RF channel model, the interference mechanism of high-power electromagnetic signals is combined on RF front-end devices, the nonlinear effects on the echo signal when high-power electromagnetic signals enter the receiver are simulated, and finally the range Doppler algorithm is used to image the echo signal and the interference signal. A method for evaluating the interference effect of high-power electromagnetic signals on airborne SAR imaging using the correlation coefficient of images was proposed. The results showed that the change in the correlation coefficient of images before and after interference relative to the interference signal ratio was consistent with the image interference effect. The correlation coefficient showed a stepped trend with the change in the interference signal ratio, and there was a saturation effect after the interference effect reached a certain level.

Analysis of Mass Wasting Processes in the Slumgullion Landslide Using Multi-Track Time-Series UAVSAR Images

The Slumgullion landslide is a large translational debris slide whose currently active part has likely been sliding for approximately 300 years. Its permanent motion and evolutionary processes have attracted the attention of many researchers. In order to study its mass wasting processes and evolution trend, the spatial–temporal displacement of the Slumgullion landslide was retrieved using an adaptive pixel offset tracking (POT) method with multi-track Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images. Based on three-dimensional displacement and slope information, we then revealed the spatial–temporal distribution of surface mass depletion or accumulation in the landslide, which provides a new perspective to analyze the evolutionary processes of landslides. The results indicate that the Slumgullion landslide had a spatially variable displacement, with a maximum displacement of 35 m. The novel findings of this study mainly include two parts. First, we found that the surface mass accumulated in the toe of the landslide and depleted in the top and middle area during the interval, which could increase the resisting force and decrease the driving force of the Slumgullion landslide. This result is compelling evidence which indicates the Slumgullion landslide should eventually tend to be stable. Second, we found that the distribution of geological structures can well explain some of the unique mass wasting in the Slumgullion landslide. The larger local mass depletion in the landslide neck area verifies that the sharp velocity increase in this region is not only caused by the reduction in width but is also significantly affected by the local normal faults. In summary, this study provides an insight into the relation between the landslide motion, mass volume change, and geological structure. The results demonstrate the great potential of multi-track airborne SAR for displacement monitoring and evolutionary analysis of landslides.

A Modified Range Doppler Algorithm for High-Squint SAR Data Imaging

The high-squint airborne Synthetic Aperture Radar (SAR) has the ability to detect the target area flexibly, and the detection swath is significantly increased compared with the side-looking SAR system. Therefore, it is of great significance to carry out research on high-precision imaging methods for high-squint airborne SAR. However, the high-squint SAR echoes have large Range Cell Migration (RCM), resulting in severe range–azimuth coupling and strong spatial variation. In this paper, a Modified Range Doppler Algorithm (MRDA) is proposed to cope with these effects introduced by the significant RCM in high-squint airborne SAR imaging. The bulk compensation preprocessing is first adopted to remove the considerable RCM and severe cross-coupling in a two-dimensional frequency domain. Then, Non-Linear Chirp Scaling (NLCS) in the range direction is utilized to equalize the range-variant chirp rate caused by the residual RCM and coupling and, therefore, the consistent range phase compensation can be fulfilled in range frequency domain. The modified correlation processing is executed to compensate the residual Doppler phase modulation, the residual RCM and the range-variant cubic phase modulation, which guarantees the characteristics of high efficiency and high precision. The simulations have demonstrated that the MRDA can focus the SAR echoes with large squint angles more effectively than the algorithms based on the Linear Range Walk Correction (LRWC) method.

A Robust and High-Precision Three-Step Positioning Method for an Airborne SAR Platform

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.

Spatially Variant Error Elimination for High-Resolution UAV SAR with Extremely Small Incident Angle

Airborne synthetic aperture radar (SAR) is susceptible to atmospheric disturbance and other factors that cause the position offset error of the antenna phase center and motion error. In close-range detection scenarios, the large elevation angle may make it impossible to directly observe areas near the underlying plane, resulting in observation blind spots. In cases where the illumination elevation angle is extremely large, the influence of range variant envelope error and phase modulations becomes more serious, and traditional two-step motion compensation (MOCO) methods may fail to provide accurate imaging. In addition, conventional phase gradient autofocus (PGA) algorithms suffer from reduced performance in scenes with few strong scattering points. To address these practical challenges, we propose an improved phase-weighted estimation PGA algorithm that analyzes the motion error of UAV SAR under a large elevation angle, providing a solution for high-order range variant motion error. Based on this algorithm, we introduce a combined focusing method that applies a threshold value for selection and optimization. Unlike traditional MOCO methods, our proposed method can more accurately compensate for spatially variant motion error in the case of scenes with few strong scattering points, indicating its wider applicability. The effectiveness of our proposed approach is verified by simulation and real data experimental results.

A Novel Filtering Method of 3D Reconstruction Point Cloud from Tomographic SAR

With the development of airborne synthetic aperture radar (SAR) technology, the 3D SAR point cloud reconstruction has emerged as a crucial development trend in the current SAR community. However, due to measurement errors, environmental interference, radar decoherence, and other noises associated with the SAR system, the reconstructed tomogram is often deteriorated by numerous noisy scatterers. As a result, it becomes challenging to obtain high-quality 3D point clouds of the observed object, making it difficult to further process the point cloud and realize target identification. To address these issues, we propose a K nearest neighbor comprehensive weighted filtering algorithm. The filtered point cloud is evaluated quantitatively using three-dimensional entropy. In this study, we adopted various filtering methods for simulated data, P-band data of Genhe, and Ku-band data of Yuncheng to refine the tomogram and compare their performances. Both qualitative and quantitative analyses demonstrate the superiority of the filtering algorithm proposed in this paper.

High-Precision Satellite Video Stabilization Method Based on ED-RANSAC Operator

Video image stabilization technology is a crucial foundation for applications such as video image target identification, monitoring, and tracking. Satellite video covers a wide range of areas with complex and similar types of objects on the ground and diverse video types. However, currently, there is a lack of a general high-precision satellite video stabilization method (VSM) that can be applied to different land cover types and imaging modes. This paper proposes a high-precision VSM based on the ED-RANSAC, an error elimination operator constrained by Euclidean distance. Furthermore, a set of accuracy evaluation methods to ensure the reliability of video stabilization are sorted out. This paper conducted video stabilization experiments using optical video data from the Jilin-01 satellite and airborne SAR video data. Under the precision evaluation criteria proposed in this paper, the optical satellite video achieved inter-frame stabilization accuracy of better than 0.15 pixels in different test areas. The overall stabilization accuracy was better than 0.15 pixels. Similarly, the SAR video achieved inter-frame stabilization accuracy better than 0.3 pixels, and the overall stabilization accuracy was better than 0.3 pixels. These experimental results demonstrate the reliability and effectiveness of the proposed method for multi-modal satellite video stabilization.

Polarimetric Decomposition and Machine Learning‐Based Classification of L‐ and S‐Band Airborne SAR (LS‐ASAR) Data

AbstractThe polarimetric Synthetic Aperture Radar (SAR) data sets have been widely exploited for land use land cover (LULC) classification due to their sensitivity to the structural and dielectric properties of the imaging target. In this study, the potential of fully polarimetric L‐ and S‐band Airborne SAR (LS‐ASAR) data sets were explored for the machine‐learning‐based classification of Urban, Vegetation, Waterbody, and Open Ground. This work was done by utilizing dual‐frequency L‐ and S‐band airborne data of Santa Barbara, California, USA, acquired under the Airborne SAR (LS ASAR) campaign, a precursor airborne mission to the space‐borne NASA‐ISRO (NISAR) mission. The LS‐ASAR polarimetric information was utilized for LULC classification using the SVM classifier. The roll‐invariant Barnes, and eigenvalue/eigenvector‐based Cloude and H/A/Alpha decomposition were implemented to retrieve the scattering parameters. The backscatter response of classes was studied, and separability analysis was done to reduce the misclassification error between six class pairs‐ Vegetation—Urban, Vegetation—Waterbody, Vegetation—Open Ground, Urban—Waterbody, Urban—Open Ground, and Water—Open Ground. The decomposition models failed to achieve the desirable separability index for all six class pairs; consequently, the classification of Barnes, Cloude, H/A/Alpha decomposition showed misclassification between vegetation‐urban class, and waterbody‐open ground class for both L‐ and S‐band data sets. The effort was made toward improving the classification accuracy by integrating the roll‐invariant and eigenvalue‐eigenvector scattering parameters of the multifrequency L‐ and S‐band data set. This method presented the desirable separability index for all class‐pair; eventually highest classification accuracy was achieved i.e. 93.35% ( = 0.91) by significantly reducing the misclassification error between class pairs.

Characterizing and Mapping Volcanic Flow Deposits on Mount St. Helens via Dual-Band SAR Imagery

Mapping volcanic flow deposits can be achieved by considering backscattering characteristics as a metric of surface roughness. In this study, we developed an approach to extract a measure of surface roughness from dual-band airborne Synthetic Aperture Radar (ASAR) backscattering data to characterize and map various volcanic flow deposits—namely, debris avalanches, lahars, lava flows, and pyroclastic density currents. We employed ASAR and Indian Space Research Organization (ISRO) airborne SAR datasets, from a joint project (ASAR-ISRO), acquired in December 2019 at 2 m spatial resolution, to assess the role and importance of incorporating dual-band data, i.e., L-band and S-band, into surface roughness models. Additionally, we derived and analyzed surface roughness from a digital surface model (DSM) generated from unoccupied aircraft systems (UAS) acquisitions using Structure from Motion (SfM) photogrammetry techniques. These UAS-derived surface roughness outputs served as meter-scale calibration products to validate the radar roughness data over targeted areas. Herein, we applied our method to a region in the United States over the Mount St. Helens volcano in the Cascade Range of Washington state. Our results showed that dual-band systems can be utilized to characterize different types of volcanic deposits and range of terrain roughness. Importantly, we found that a combination of radar wavelengths (i.e., 9 and 24 cm), in tandem with high-spatial-resolution backscatter measurements, yields improved surface roughness maps, compared to single-band, satellite-based approaches at coarser resolution. The L-band (24 cm) can effectively differentiate small, medium, and large-scale structures, namely, blocks/boulders from fine-grained lahar deposits and hummocks from debris avalanche deposits. Additionally, variation in the roughness estimates of lahar and debris avalanche deposits can be identified and quantified individually. In contrast, the S-band (9 cm) can distinguish different soil moisture conditions across variable terrain; for example, identify wet active channels. In principle, this dual-band approach can also be employed with time series of various other SAR data of higher coherence (such as satellite SAR), using different wavelengths and polarizations, encompassing a wider range of surface roughness, and ultimately enabling additional applications at other volcanoes worldwide and even beyond volcanology.