Synthetic Aperture Radar Applications Research Articles

Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites

Abstract. Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 (S1) SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, subweekly temporal resolution and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–2021 at six study sites across the western United States as part of the NASA SnowEx mission. Across all sites, we find agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements to be considerably lower than requirements placed for remotely sensed observations of snow depth, with a mean root mean square error (RMSE) of 0.92 m and a mean Pearson correlation coefficient r of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross- to co-backscatter ratio (σVH/σVV; i.e., cross ratio) relative to lidar snow depths. We find the cross ratio increases through the time series for snow depths over ∼ 1.5 m but that the cross ratio decreases for snow depths less than ∼ 1.5 m. We attribute poor algorithm performance to (a) the variable amount of apparent snow depth signal in the S1 cross ratio and (b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source framework for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more research into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume-scattering-based retrievals.

GB-SAR geocoding considering the radar attitude inclination angles for building facade deformation extraction

ABSTRACT The process of engineering construction typically impacts the nearby existing buildings, causing either localized or widespread deformation of these structures. The application of deformation monitoring technology enables the timely identification of damage or uneven deformation, thereby facilitating the implementation of appropriate maintenance and repair actions. This paper explores the application of ground-based synthetic aperture radar (GB-SAR) in building facade deformation monitoring. GB-SAR acquires two-dimensional (2D) images and measures deformation along the line of sight (LOS) direction through interference calculation. In order to accurately determine the location of deformation and retrieve the elevation information of image pixels, it is usually required to utilize external three-dimensional (3D) data to reconstruct the 3D coordinate of these pixels, which is known as geocoding, or georeferencing. In the case of the GB-SAR system, it is necessary for the observer to measure the angles between the antenna orientation, the radar movement direction and the reference plane of the local surveying coordinate system. Nevertheless, the measurement of these attitude angles may be overlooked in practice, and the impact of small attitude angles or their measurement errors on GB-SAR geocoding may be disregarded, which directly affects the slant range projection (SRP) calculation of the external 3D data, thereby leading to biased 3D coordinate reconstruction of GB-SAR pixels. This paper proposes a method to calculate the SRP coordinates of point cloud data considering the attitude inclination angles of radar sensor, and perform matching calculations on the high-quality pixels (HQPs) with the point cloud after SRP to extract building facade deformation. The results demonstrate that the method is capable of effectively mitigating the influence of the radar attitude inclination angles, resulting in a reduction of the average deviation of 3D coordinates to approximately one-third of the value without consideration of the sensor’s attitude in a real-world building monitoring experiment.

Multi‐objective terminal trajectory optimization based on hybrid genetic algorithm pseudospectral method

AbstractDuring terminal guidance, the attack platform is provided with a high‐resolution image of the target area through the application of synthetic aperture radar. Additionally, the stealth trajectory with low observability can significantly impact mission success. This paper considers both the performance of missile‐borne synthetic aperture radar imaging and stealth performance as influencing factors for terminal trajectory optimization, which is modelled as a constrained multi‐objective optimization problem. The application of the pseudospectral method in the solution of optimal control problems has led to the proposal of the hybrid genetic algorithm pseudospectral optimization framework. The problem is decomposed into several single‐objective optimal control problems, which can generate a specific initial population for the genetic algorithm to obtain a set of Pareto‐optimal solutions. Finally, the numerical simulations demonstrate the effectiveness of the proposed optimization approach compared with the benchmark scheme.

CFAR Target Detector in Synthetic Aperture Radar

Introduction. Constant false alarm rate (CFAR) detectors have found application in synthetic aperture radar (SAR) systems. The operating principle of a classic cell averaging detector (CA-CFAR detector) is based on comparing the decision statistics in the test resolution element with an adaptive threshold, which is calculated from signals in the reference cells. The decision statistic is an estimate of the signal power. Therefore, target signal detection is based on the brightness contrast of the test and reference resolution cells. Such a detector is optimal provided that the noise background is homogeneous. In cases where the background homogeneity is violated, the quality of detection deteriorates. There are several known methods for improving the quality of detection (GO-CFAR, SO-CFAR, OS-CFAR, etc.). However, the precise principle of detection by brightness contrast in such CFAR detectors remains unchanged.Aim. To synthesize a CFAR detector that uses not only the brightness contrast between the test and reference resolution cells, but also the spectral differences of the signals.Materials and methods. The proposed CFAR detector uses estimates of the algebraic moments of the power spectral density of signals in range cells, based on which three decision statistics are calculated containing information about the power, the position of the energy center, and the width of the signal spectrum. The decision about the presence of a target in the test cell is carried out according to the 2/3 rule (2 threshold overshoots out of 3 comparisons).Results. A comparison of the proposed detector with the SO-CFAR detector, performed by computer simulation, showed that, under a signal-to-clutter ratio of -6 dB and a false alarm probability of 10-4, the detection probability of the proposed detector was 0.933 versus 0.708 for the SO-CFAR detector.Conclusion. The article proposes a three-parameter CFAR detector for a synthetic aperture radar system, in which the decision on the presence of a target in the test cell is made via estimation of the first three algebraic moments of the signal spectrum. The synthesized detection algorithm can also be used when detecting moving targets in SAR.

A Power Combiner–Splitter Based on a Rat-Race Coupler for an IQ Mixer in Synthetic Aperture Radar Applications

Synthetic aperture radar (SAR) is a powerful tool in remote sensing applications that can produce high-resolution images and operate in any weather condition. It is composed of many RF components, such as the IQ mixer, which mixes the base chirp signal (IF) with the carrier signal (LO) and increases the bandwidth of the transmitted signal to twice the maximum frequency of the base chirp signal, reducing the workload of Programmable Field Gate Arrays (FPGA) and increasing the resolution of the SAR system. This research proposes a power combiner–splitter design that will be used as a supporting component to construct the IQ mixer in SAR applications based on a rat-race coupler. The measurement results show that the coupler has good S-parameter values. S11, S22, and S33 have a low reflection value below −17 dB, S13 has a high isolation value below −22 dB, and S21 and S31 have a low attenuation value below −4 dB with amplitude unbalance below 0.1 dB and phase unbalance below 1∘. The 150 MHz requirement bandwidth for the RF signal is also achieved.

MSSD-Net: Multi-Scale SAR Ship Detection Network

In recent years, the development of neural networks has significantly advanced their application in Synthetic Aperture Radar (SAR) ship target detection for maritime traffic control and ship management. However, traditional neural network architectures are often complex and resource intensive, making them unsuitable for deployment on artificial satellites. To address this issue, this paper proposes a lightweight neural network: the Multi-Scale SAR Ship Detection Network (MSSD-Net). Initially, the MobileOne network module is employed to construct the backbone network for feature extraction from SAR images. Subsequently, a Multi-Scale Coordinate Attention (MSCA) module is designed to enhance the network’s capability to process contextual information. This is followed by the integration of features across different scales using an FPN + PAN structure. Lastly, an Anchor-Free approach is utilized for the rapid detection of ship targets. To evaluate the performance of MSSD-Net, we conducted extensive experiments on the Synthetic Aperture Radar Ship Detection Dataset (SSDD) and SAR-Ship-Dataset. Our experimental results demonstrate that MSSD-Net achieves a mean average precision (mAP) of 98.02% on the SSDD while maintaining a compact model size of only 1.635 million parameters. This indicates that MSSD-Net effectively reduces model complexity without compromising its ability to achieve high accuracy in object detection tasks.

Synthetic Aperture Radar in Vineyard Monitoring: Examples, Demonstrations, and Future Perspectives

Synthetic aperture radar (SAR) technology has emerged as a pivotal tool in viticulture, offering unique capabilities for various applications. This study provides a comprehensive overview of the current state-of-the-art applications of SAR in viticulture, highlighting its significance in addressing key challenges and enhancing viticultural practices. The historical evolution and motivations behind SAR technology are also provided, along with a demonstration of its applications within viticulture, showcasing its effectiveness in various aspects of vineyard management, including delineating vineyard boundaries, assessing grapevine health, and optimizing irrigation strategies. Furthermore, future perspectives and trends in SAR applications in viticulture are discussed, including advancements in SAR technology, integration with other remote sensing techniques, and the potential for enhanced data analytics and decision support systems. Through this article, a comprehensive understanding of the role of SAR in viticulture is provided, along with inspiration for future research endeavors in this rapidly evolving field, contributing to the sustainable development and optimization of vineyard management practices.

Different data-driven prediction of global ionospheric TEC using deep learning methods

Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present the random forest method and the Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict the global TEC by incorporating the geomagnetic and solar activity parameters. Two schemes with different ionospheric data, i.e., spherical harmonic coefficients (SHC) and vertical TEC (VTEC), are performed by Autoformer-MLP. Ionospheric products from 2009 to 2019 (Cycle 24), obtained from the Center for Orbit Determination in Europe (CODE), are collected to test and evaluate the proposed method. Experimental results demonstrate that the root mean square errors (RMSEs) of predicted global ionospheric maps (GIMs) using the SHC scheme are 3.79 and 1.39 TECU in 2015 and 2019, respectively, and those are 3.55 and 1.28 TECU for the VTEC scheme. Moreover, the RMSEs of the prediction results are 0.5 and 0.2 TECU lower than that of CODE's 1-day predicted global ionospheric map (GIM) product (C1PG) during high and low solar activity years, respectively. These analyses indicate that the proposed random forest and Autoformer-MLP deep learning methods exhibit high accuracy and stability for data-driven prediction of global ionospheric TEC in various scenarios.

The Prospect of Typhoon Landfall Observation Utilizing L-band InSAR

The cyclonic phenomenon, particularly typhoons within the 100° E to 180° E longitude region, bring significant devastation through intense rainfall and strong winds, causing secondary effects like landslides and floods upon landfall. This results in infrastructural damage and loss of life, ranking storm-related disasters, including typhoons, second only to earthquakes in casualties, emphasizing the need for a robust observation system. While higher income countries benefit from established radar and meteorological stations for continuous monitoring, this study highlights the critical necessity for an additional monitoring system in lower income countries. We investigate the applicability of Interferometric Synthetic Aperture Radar (InSAR) during typhoon landfall, particularly in densely vegetated areas where longer wavelengths like L-band (for instance, the Advanced Land Observing Satellite (ALOS/PALSAR) and ALOS-2/PALSAR-2) are suitable. By analyzing typhoon occurrences and confirming landfall using weather radar from 2007 to 2011 (ALOS/PALSAR observation period) and 2014 to 2023 (ALOS-2/PALSAR-2 observation period), our study successfully identifies the landfall of the Jebi typhoon on September 4, 2018. InSAR processing reveals significant phase gradients linked to dense water vapor during landfall, corroborated by comparisons with sea-level Zenith Wet Delay (ZWD) from Global Navigation Satellite System (GNSS) data and hourly precipitation rates from the Automated Meteorological Data Acquisition System (AMeDAS). The concurrence of InSAR results with ZWD values and rainfall data from meteorological stations validates the potential of L-band InSAR in observing typhoon landfalls.

Transfer-Aware Graph U-Net with Cross-Level Interactions for PolSAR Image Semantic Segmentation

Although graph convolutional networks have found application in polarimetric synthetic aperture radar (PolSAR) image classification tasks, the available approaches cannot operate on multiple graphs, which hinders their potential to generalize effective feature representations across different datasets. To overcome this limitation and achieve robust PolSAR image classification, this paper proposes a novel end-to-end cross-level interaction graph U-Net (CLIGUNet), where weighted max-relative spatial convolution is proposed to enable simultaneous learning of latent features from batch input. Moreover, it integrates weighted adjacency matrices, derived from the symmetric revised Wishart distance, to encode polarimetric similarity into weighted max-relative spatial graph convolution. Employing end-to-end trainable residual transformers with multi-head attention, our proposed cross-level interactions enable the decoder to fuse multi-scale graph feature representations, enhancing effective features from various scales through a deep supervision strategy. Additionally, multi-scale dynamic graphs are introduced to expand the receptive field, enabling trainable adjacency matrices with refined connectivity relationships and edge weights within each resolution. Experiments undertaken on real PolSAR datasets show the superiority of our CLIGUNet with respect to state-of-the-art networks in classification accuracy and robustness in handling unknown imagery with similar land covers.

Lightweight high output power C-band FMCW SIMO radar system for small drones

ABSTRACT Radar technology is critical for subsurface sensing applications since it enables the extraction of surface information remotely without being affected by environmental factors such as illumination and weather conditions. Hence, their usage on drones has become extremely essential as these vehicles need to accumulate data while flying over different and possibly difficult-to-access terrains regardless of visibility conditions. A frequency-modulated continuous-wave (FMCW) single input multiple output (SIMO) radar system is developed to provide near real-time radar images onboard. The system is comprised of a transceiver module, specifically designed for synthetic aperture radar (SAR) applications, to produce images in real-time. GPU-based computation is utilized to optimize processing power, while enabling range profiles to be generated and SIMO SAR images to be created during flight. The system can be easily mounted on UAV or multirotor systems. The detection and ranging performance of the system are examined by performing several experiments at the campus. Objects up to 100 metres are successfully detected by the radar system. The system demonstrates excellent ability to generate 2D SIMO SAR images, effectively identifying building and guard-box exteriors that are consistent with images from Google Earth.

A Lightweight Arbitrarily Oriented Detector Based on Transformers and Deformable Features for Ship Detection in SAR Images

Lightweight ship detection is an important application of synthetic aperture radar (SAR). The prevailing trend in recent research involves employing a detection framework based on convolutional neural networks (CNNs) and horizontal bounding boxes (HBBs). However, CNNs with local receptive fields fall short in acquiring adequate contextual information and exhibit sensitivity to noise. Moreover, HBBs introduce significant interference from both the background and adjacent ships. To overcome these limitations, this paper proposes a lightweight transformer-based method for detecting arbitrarily oriented ships in SAR images, called LD-Det, which excels at promptly and accurately identifying rotating ship targets. First, light pyramid vision transformer (LightPVT) is introduced as a lightweight backbone network. Built upon PVT v2-B0-Li, it effectively captures the long-range dependencies of ships in SAR images. Subsequently, multi-scale deformable feature pyramid network (MDFPN) is constructed as a neck network, utilizing the multi-scale deformable convolution (MDC) module to adjust receptive field regions and extract ship features from SAR images more effectively. Lastly, shared deformable head (SDHead) is proposed as a head network, enhancing ship feature extraction with the combination of deformable convolution operations and a shared parameter structure design. Experimental evaluations on two publicly available datasets validate the efficacy of the proposed method. Notably, the proposed method achieves state-of-the-art detection performance when compared with other lightweight methods in detecting rotated targets.

Reduced Navigation Error Using a Multi-Sensor Fusion Technique and Its Application in Synthetic Aperture Radar

Reduced Navigation Error Using a Multi-Sensor Fusion Technique and Its Application in Synthetic Aperture Radar

DESIGN OF AMPLITUDE AND PHASE CONTROLLED BUTLER BEAMFORMING NETWORK FOR X/KU-BAND AIRBORNE SYNTHETIC APERTURE RADAR APPLICATIONS

Beamforming networks play an important role in today's communication systems. The Butler matrix network is one of the most useful networks used to feed array antennas and generate multi-beamforming systems. Its complexity increases with an increase in the number of input and output ports. This research work presents simulated results of an unequal amplitude 8 × 8 Butler matrix network, in which the Taylor series synthesis technique was employed to generate unequal amplitudes (-9.626, -9.125, -8.798, -8.636, -8.636, -8.798, -9.125, and -9.626 dB). The design of the Butler matrix beamforming network is based on two frequency bands, i.e., the X-band and Ku-band, with center frequencies of 9.3 and 13.265 GHz, respectively. The design mainly involves 12 hybrid couplers and eight phase shifters that can generate beams in eight different directions (-32.4°, -26.5°, -15.5°, -10.3°, 10.3°, 15.5°, 26.5°, and 32.4°). This design is based on the Taylor series expansion, which has the advantage of sidelobe level reduction of -17 dB. All of the simulations were done using advanced design system software key sight technologies. The simulations results were in line with the theoretical calculations.

AN X/Ku DUAL-BAND SHARED APERTURE UNIFORM LINEAR ANTENNA ARRAY FOR AIRBORNE SYNTHETIC APERTURE RADAR APPLICATIONS

An amplitude distribution of shared aperture antenna array for airborne SAR applications using a dual-band (X/Ku-band) synthesis is presented in this research. It is utilizing a linear arrangement with 1 × 8 antenna elements and a series feeding mechanism to operate in the X/Ku bands. The proposed shared aperture antenna array has eight elements providing a uniform array in the X and Ku bands. To realize the proposed scanning array, the X/Ku-band antenna array takes an inter-element spacing of 0.7 λ, which provides a scanning angle of ± 25°. When the space between adjacent antenna arrays is effectively used to reduce the influence of mutual coupling, the synthesis array's X-band and Ku-band antenna arrays can perform well in isolation. The uniform linear array exhibits Side Lobe Level (SLL) of -23.9 dB in X-Band and -24.7 dB in Ku-Band, with maximum realized gains of 14.2 dBi in X-Band and 15.7 dBi in Ku-Band. The isolation between bands and ports exceeds 25 dB. The uniform amplitude distribution shared aperture antenna (UAD SAA) has accepted an SLL performance of -13.5 dB. Additionally, it displays a uniform (X/Ku-band 200/144 MHz) impendence bandwidth that is relatively wide.

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan–Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase–height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics.

Monitoring maize lodging severity based on multi-temporal Sentinel-1 images using Time-weighted Dynamic time Warping

Lodging is one of the most important stresses in maize growth, which affects the yield, quality and mechanical harvesting ability of maize. Monitoring the lodging range and severity of large-scale maize by satellite remote sensing technology can support agricultural insurance claims, planting structure adjustment and field management. The purpose of this study is to investigate the application of Synthetic Aperture Radar (SAR) remote sensing images in monitoring the severity of maize lodging at the county scale. A time series feature dataset was obtained from multi-temporal Sentinel-1 SAR images before and after lodging. The sensitive features of maize lodging severity were screened by Jeffreys-Matusita (J-M) distance. The study compared the performance of three different methods for monitoring maize lodging severity, including Time-weighted Dynamic Time Warping (TwDTW), random forest (RF) and minimum distance (MinD). The results showed that all time phases of SAR images selected in the study had an important contribution to the differentiation of maize lodging severities. Among all features, the VH (Vertical Send and Horizontal Receive) time series features composed of five time-phase had better discrimination for different maize lodging severities. The accuracy of extracting maize lodging severities based on TwDTW was 76% and the Kappa coefficient was 0.70, which was higher than RF and MinD. Thus, by utilizing the multi-time phase features of Sentinel-1, we can effectively monitor the grades of maize lodging at the county scale.

Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering

Ship target detection is an important application of synthetic aperture radar (SAR) imaging remote sensing in ocean monitoring and management. However, SAR imaging is a form of coherence imaging, meaning that there is a large amount of speckle noise in each SAR image. This seriously affects the detection of an SAR image ship target when the fuzzy C-means (FCM) clustering method is used, resulting in numerous errors and incomplete detection. It is also associated with a slow detection speed, which easily falls into the local minima. To overcome these issues, a new method based on block thumbnail particle swarm optimization clustering (BTPSOC) was proposed for SAR image ship target detection. The BTPSOC algorithm uses block thumbnails to segment the main pixels, which improves the resistance to noise interference and segmentation accuracy, enhances the ability to process different types of SAR images, and reduces the runtime. When particle swarm optimization (PSO) technology is used to optimize the FCM clustering center, global optimization is achieved, the clustering performance is improved, the risk of falling into the local minima is overcome, and the stability is improved. The SAR images from two datasets containing ship targets were used in verification experiments. The experimental results show that the BTPSOC algorithm can effectively detect the ship target in SAR images and that it maintains good integrity with regard to the detailed information from the target region. At the same time, experiments comparing the deep convolution neural network (CNN) and constant false alarm rate (CFAR) were conducted.

Origami-inspired self-deployable reflectarray antenna

Reflector antennas, operating at high frequencies, require a high surface accuracy: deviations from the target geometry accuracy result in a loss of RF performance (e.g., antenna gain and efficiency). As a result, the aperture may need to be larger, or a more complex backing structure must be engineered to achieve the reflector surface accuracy to deliver the equivalent gain of an efficient solid reflector. This would typically increase the mass, the stowed volume, and the design complexity. The design of larger aperture deployable antennas (e.g., Cassegrain, Offset) for SAR (Synthetic Aperture Radar) applications can be challenging for missions where platform size and payload mass are limited.Inspired by the Origami based techniques, Oxford Space Systems (OSS) is currently developing an innovative SAR Reflectarray antenna, targeting the X-Band frequency range, with the aim of achieving a relatively large aperture from a small, stowed volume suitable for a small satellite. The Reflectarray antenna architecture has several potential advantages over other antenna designs. It is a cost-competitive technology with excellent stowage efficiency, which can be further improved with origami-based folding mechanisms/kinematics. It requires a simplified deployment mechanism and in contrast to conventional parabolic reflectors, it does not need a separate backing structure.Until now, work on space deployable Reflectarray antennas (e.g., MARCO, OMERA and ISSARA missions from NASA) have focused on multi-degree of freedom structures, i.e., independent self-actuated hinge driven architectures. This paper presents an elegant single degree of freedom, origami-inspired Reflectarray architecture that does not require actuation on all the hinges. Thanks to its Hold-Down Release Mechanism (HDRM) and spring-loaded hinges, the OSS Reflectarray antenna is self-deployable. It maintains the required flatness accuracy achieved through its novel hinge integrated latching system.The OSS Reflectarray antenna has an 8:1 footprint stowage ratio, which enables installation on small satellite platforms (between 50 kg and 100 kg). The reflectarray has been designed to work in the X-band frequency with dual linear polarization for the concept study. However, the architecture can be scaled up or down to suit other frequency bands, making the OSS Reflectarray compatible with many applications and platforms.

Robust Interferometric ISAR Imaging With UAMP-Based Joint Sparse Signal Recovery

Sparse Bayesian learning (SBL) has found successful applications in interferometric inverse synthetic aperture radar (InISAR) imaging, especially in the presence of limited number of pulses or when using sparse apertures. SBL-based InISAR algorithms have been proven to be significantly superior to Fourier transform-based ones. However, the existing SBL-based algorithms are slow due to their high computational complexity. Moreover, there is also much room to improve in terms of imaging performance. In this work, leveraging the approximate message passing with unitary transformation (UAMP), we propose an InISAR imaging algorithm named UAMP-JSR (joint sparse recovery), which is much faster and delivers notably higher imaging accuracy than the existing SBL-based algorithms. Specifically, we develop a type-2 joint sparse model (JSM-2) for InISAR imaging and formulate it as a two-layer multiple measurement vectors (MMV) joint sparse problem. Based on a factor graph representation, the message passing techniques are used to efficiently solve this problem, which leads to the UAMP-JSR algorithm. Results based on extensive simulations and experiments based on the real data collected by the Pisa Radar (PIRAD) demonstrate the effectiveness and superiority of the proposed algorithm compared to existing algorithms.