Multiple Synthetic Aperture Radar Research Articles

NRENet: Neighborhood removal-and-emphasis network for ship detection in SAR Images

In recent years, object detection in Synthetic-Aperture Radar (SAR) images still faces many challenges, especially for ship detection. Small or dense ships are vulnerable to the interference of complex scenes such as ports and land. In feature extraction, a large amount of redundant information on the feature map will further reduce the network’s attention to small-sized ships. Therefore, in this paper, we propose a network called neighborhood removal-and-emphasis network (NRE-Net), including an object neighborhood removal (ONR) strategy and a neighborhood feature emphasis (NFE) module. Among them, the ONR strategy directly removes the complex background information around the ship before feature extraction, only retaining effective contextual neighborhoods conducive to ship detection and avoiding the interference of complex background information on the network. The NFE module is based on ONR to extract features and form a weight map of small-sized ships or complex images. This module can adaptively recognize the detection neighborhood of each ship and highlight the detection ship on the feature map. Our network has validated the effectiveness of the method on multiple SAR ship datasets and improved the object AP for each size. Our code is available at: https://github.com/Xidian-AIGroup190726/NRENet.

A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic

In the mosaic creation of multiple high-resolution synthetic aperture radar (SAR) images, achieving an optimal seamline in overlapping areas is crucial for seamless and visually satisfactory results. Many existing seamline generation methods are designed primarily for optical remote sensing images, but due to the differing characteristics of SAR images and optical images, applying these methods directly to SAR images poses challenges in finding the optimal seamline. In response, this paper proposes a segmentation-based optimal seamline generation (SOSG) method for SAR image mosaics. The SOSG method involves a multi-step process. First, SAR image joint segmentation is performed within the overlapping areas. Subsequently, homogeneous areas are identified based on the segmentation results. Following this, a pixel cost matrix is constructed, incorporating homogeneous areas and intensity differences. Finally, the minimum path cost from the starting pixel to the end pixel is computed using the Dijkstra algorithm to determine the optimal path. To assess the feasibility and effectiveness of the proposed method, experiments are conducted using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets. The experimental results demonstrate that the proposed method yields seamless mosaic images when compared to other methods, while delivering satisfactory outcomes. This indicates the potential of the proposed method in addressing the unique challenges posed by SAR images and enhancing the quality of SAR image mosaics.

GPU Acceleration for SAR Satellite Image Ortho-Rectification

Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric positioning models for SAR images. In order to address the high computation time of pixel-by-pixel ortho-rectification in SAR satellite images, a GPU-accelerated pixel-by-pixel correction method based on a rational polynomial coefficients (RPCs) model is proposed, which improves the efficiency of SAR satellite image ortho-rectification. Furthermore, in order to solve the problem of linearly increasing processing time for the ortho-rectification of multiple SAR images in large overlapping areas, a multi-GPU collaborative acceleration strategy for the ortho-rectification of multiple SAR images in large overlapping areas is proposed, achieving efficient ortho-rectification processing of multiple SAR image data in large overlapping areas. By conducting ortho-rectification experiments on 20 high-resolution SAR images from the Gaofen-3 satellite, the feasibility and efficiency of the multi-GPU collaborative acceleration processing algorithm are verified.

Multi-View Data-Based Layover Information Compensation Method for SAR Image Mosaic

Currently, massive Synthetic Aperture Radar (SAR) images acquired from numerous SAR satellites have been widely utilized in various fields, and image mosaicking technology provides important support and assistance for these applications. The traditional mosaic method selects specific SAR images that can cover the region of interest (ROI) from redundant data to produce “One Map”. However, an SAR image suffers from severe geometric distortion, especially in mountainous areas, which inevitably reduces the utilization of mosaic image. Therefore, a multi-view data-based layover information compensation (MDLIC) method for SAR image mosaic is proposed, aiming to take full advantage of multi-view data to compensate for the missing information in the layover area of the SAR image. This is performed to improve the information content of the mosaic image and realize efficient thematic information extraction and analysis. First, the calculation of the object-space extent of all images and the division of object-space grid are completed on the basis of geometric and radiometric preprocessing. Then, according to the transformation relationship between the object-space and the image-space, the sampling rate image of each image corresponding to the object-space grid is generated, which determines the layover area and the layover degree in each image. Finally, the information compensation strategy is implemented in accordance with the sampling rate image to realize the compensation of the layover information. The feasibility and effectiveness of the MDLIC method are verified by using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets for experiments. The experimental results indicate that the MDLIC method can obtain mosaic images with richer information compared with the traditional method, while still providing satisfactory results.

Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region.

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east-west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.

Evolution of the magma system at Makushin volcano, Alaska, from 2004 to 2021

Located in the northeast of Unalaska Island in the central part of the Aleutian, Alaska, Makushin volcano is one of the more active volcanoes in the United States, with the most recent eruption occurred in 1995. Pre-eruptive inflation and post-eruptive deflation were identified from 1993 to 1995 and 1996 to 2000, respectively, and were interpreted as volume changes associated with magma movement within the plumbing system. In this study, we investigate the surface deformation of Makushin from 2004 to 2021 to evaluate its present-day activity, using Synthetic Aperture Radar (SAR) interferometry technique. SAR images acquired from C-band Envisat and Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 satellites are analyzed using an advanced multiple temporal Interferometric SAR (InSAR) algorithm. The produced deformation time series are then used to track the temporal evolution of the volcanic system with Ensemble Kalman Filter (EnKF) assuming Mogi sources. The results show that Makushin is characterized by time-varying deformation episodes including quiescence, and inflation/deflation cycles at varied rates since 2004. The derived deformation histories can be well reproduced by a spatially stationary pressure source located to the east-northeast of the summit, at a depth about 6 km beneath the sea level (BSL). The inflation/deflation cycles with distinct temporal patterns are interpreted as results from volatile-dominated volume accumulation/loss in the magmatic reservoir. A secondary reservoir connected to the main magma chamber is revealed from the deformation time series. No surface displacement associated with the large earthquake swarm in 2020 is identified from the InSAR-derived deformation. The cumulative volume change in the magma reservoir is about −6×106m3 from 2004 to 2021, indicating a volume/pressure deficit in the magma plumbing system.

A cross‐cross‐correlation based method for joint coregistration of rotated multitemporal synthetic aperture radar images

AbstractCoregistration is among the most important and challenging tasks when dealing with multiple synthetic aperture radar (SAR) images, especially when they are acquired at different time instants and characterised by low signal to noise power ratio (SNR) that contributes to their coherence reduction. However, even if some technological expedients could be implemented to maintain the same trajectory and to compensate for these inaccuracies during the acquisition campaign, multitemporal SAR images always need additional registration refinements after compression. Usually, to coregister a series of multitemporal SAR images, one of them is selected as the master, and the remainders are separately registered to it. Differently, in this study, a new strategy is developed to jointly coregister a stack of multitemporal SAR images. It is based on the exploitation of the cross‐correlations in turn computed from each couple of cross‐correlations (a.k.a. cross‐cross‐correlations) of the extracted patches. By doing so, the method is capable of exploiting also the respective misregistration information between the slaves during the estimation process. In this respect, this methodology is applied to enhance the registration capabilities of the constrained Least Squares (CLS) optimisation method, which instead does not account for the reciprocal information related to the slaves. Several tests are performed on multitemporal airborne‐measured SAR data. Obtained results show the effectiveness of the proposed algorithm in terms of achieved root mean square error for images affected by respective rotations also in comparison with the CLS counterpart.

An Effective Multi-Layer Attention Network for SAR Ship Detection

The use of deep learning-based techniques has improved the performance of synthetic aperture radar (SAR) image-based applications, such as ship detection. However, all existing methods have limited object detection performance under the conditions of varying ship sizes and complex background noise, to the best of our knowledge. In this paper, to solve both the multi-scale problem and the noisy background issues, we propose a multi-layer attention approach based on the thorough analysis of both location and semantic information. The solution works by exploring the richness of spatial information of the low-level feature maps generated by a backbone and the richness of semantic information of the high-level feature maps created by the same method. Additionally, we integrate an attention mechanism into the network to exclusively extract useful features from the input maps. Tests involving multiple SAR datasets show that our proposed solution enables significant improvements to the accuracy of ship detection regardless of vessel size and background complexity. Particularly for the widely-adopted High-Resolution SAR Images Dataset (HRSID), the new method provides a 1.3% improvement in the average precision for detection. The proposed new method can be potentially used in other feature-extraction-based classification, detection, and segmentation.

A Scheduling Method of Using Multiple SAR Satellites to Observe a Large Area

This paper presents a scheduling problem of using multiple synthetic aperture radar (SAR) satellites to observe a large irregular area (SMA). SMA is usually considered as a kind of nonlinear combinatorial optimized problem and its solution space strongly coupled with geometry grows exponentially with the increasing magnitude of SMA. It is assumed that each solution of SMA yields a profit associated with the acquired portion of the target area, and the objective of this paper is to find the optimal solution yielding the maximal profit. The SMA is solved by means of a new method composed of three successive phases, namely, grid space construction, candidate strip generation and strip selection. First, the grid space construction is proposed to discretize the irregular area into a set of points in a specific plane rectangular coordinate system and calculate the total profit of a solution of SMA. Then, the candidate strip generation is designed to produce numerous candidate strips based on the grid space of the first phase. At last, in the strip selection, the optimal schedule for all the SAR satellites is developed based on the result of the candidate strip generation. In addition, this paper proposes a normalized grid space construction algorithm, a candidate strip generation algorithm and a tabu search algorithm with variable neighborhoods for the three successive phases, respectively. To verify the effectiveness of the proposed method in this paper, we perform simulation experiments on several scenarios and compare our method with the other seven methods. Compared to the best of the other seven methods, our proposed method can improve profit by 6.38% using the same resources.

Building profile reconstruction based on double subspace TomoSAR

ABSTRACT Synthetic aperture radar (SAR) tomography (TomoSAR) is an attractive technology for the three-dimensional (3D) reconstruction of buildings through multiple coherent SAR images. This technique can overcome the limitations of interferometry in multi-scatterer resolution and play a significant role in the analysis of urban areas. The robustness of the many advanced nonparametric spectral estimation approaches is founded on a high number of observations. In this work, we evaluate the abilities of five nonparametric spectral estimation methods, including double subspace (DS), maximum entropy (ME), minimum norm (MN), Capon, and beamforming (BF), to separate scattering contributions with a small number of SAR images in the tomographic reconstruction of urban environments. DS is a high-resolution estimator based on the existence of non-uniform noise, which assesses the noise subspace via eigendecomposition of some appropriately designed matrix without comprehending the noise covariance matrix. The study results indicate that DS can distinguish the scatterers in one azimuth-range resolution and satisfactorily dissuade the appearance of unpleasant side lobes. To investigate the effectiveness of DS and compare that with other expressed methods, both simulated and real SAR datasets were acquired from TerraSAR-X strip-map images of the city of Tehran, Iran. The experimental results on real SAR images demonstrate that the DS can remarkably enhance building reconstruction in the urban environment. The estimated height of the scatters utilizing the DS technique is similar to the data obtained from ground observations.

3-D Stereo Geolocation of Radar Reflectors Using Multiaspect SAR Acquisitions

With well-controlled orbits and a more accurate timing system of the new-generation synthetic aperture radar (SAR) satellites, the ability of high-precision geolocation with multiple SAR acquisitions has been verified based on radar reflectors. In this letter, SAR absolute and differential geolocation methods are introduced and implemented to retrieve the coordinates of two different types of radar reflectors: corner reflectors (CRs) and multidirectional dihedral reflectors (MDRs). The experimental results demonstrate that decimeter- even centimeter-level positioning accuracy can be obtained by SAR stereo geolocation based on multiaspect SAR acquisitions from RadarSAT-2 (RS-2), COSMO-SkyMed (CSK), Sentinel-1 (S1), and TerraSAR-X (TSX), respectively.

SAR Image Fusion Classification Based on the Decision-Level Combination of Multi-Band Information

Synthetic aperture radar (SAR) is an active coherent microwave remote sensing system. SAR systems working in different bands have different imaging results for the same area, resulting in different advantages and limitations for SAR image classification. Therefore, to synthesize the classification information of SAR images into different bands, an SAR image fusion classification method based on the decision-level combination of multi-band information is proposed in this paper. Within the proposed method, the idea of Dempster–Shafer evidence theory is introduced to model the uncertainty of the classification result of each pixel and used to combine the classification results of multiple band SAR images. The convolutional neural network is used to classify single-band SAR images. Calculate the belief entropy of each pixel to measure the uncertainty of single-band classification, and generate the basic probability assignment function. The idea of the term frequency-inverse document frequency in natural language processing is combined with the conflict coefficient to obtain the weight of different bands. Meanwhile, the neighborhood classification of each pixel in different band sensors is considered to obtain the total weight of each band sensor, generate weighted average BPA, and obtain the final ground object classification result after fusion. The validity of the proposed method is verified in two groups of multi-band SAR image classification experiments, and the proposed method has effectively improved the accuracy compared to the modified average approach.

An Efficient Radio Frequency Interference Mitigation Algorithm in Real Synthetic Aperture Radar Data

As a wideband radar system, a synthetic aperture radar (SAR) may conflict with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, termed as radio frequency interference (RFI), may severely interfere SAR systems from generating a high-resolution image. Numerous previous researches focused on the RFI suppression problem, among which the semiparametric methods have been verified to have the state-of-the-art performance. However, most of the semiparametric methods are computationally expensive and can hardly be used on wide-swath SAR imaging processing. In this article, an efficient semiparametric algorithm is proposed to suppress RFIs via alternating projections. It has comparable performance as the other methods but significantly improves the computational efficiency a lot. It is able to remove both narrowband and wideband RFIs and can be used directly on the Level-1 SAR data. Finally, multiple real SAR data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

Evaluation and Improvement of Generalization Performance of SAR Ship Recognition Algorithms

As artificial intelligence continues to advance, deep learning has greatly contributed to the advancement of ship recognition using Synthetic Aperture Radar (SAR) images. Deep learning-based SAR ship recognition performance is largely dependent on the sample set used. SAR ship recognition datasets published in recent years, however, are most derived from a single SAR satellite sensor. It needs to be evaluated and analyzed carefully whether the model trained by a single satellite dataset can still achieve the same accuracy with different SAR satellites. The paper focuses on the following research to address these issues. Firstly, using multiple SAR satellite sensors, we create a new SAR ship dataset (named generalization performance evaluation dataset, GPED) containing multi-resolution and multi-polarization data to examine the generalization performance of the deep learning-based SAR ship recognition method. GPED and a marine target detection dataset (MTDD) are then used to evaluate and analyze the generalization performance of current mainstream deep learning methods. According to the the experiment results, the mean average accuracy (mAP) of the ship recognition model trained on GPED is generally higher than that of MTDD, which proves that GPED has a better generalization performance. Furthermore, SAR ship detection datasets have more samples than ship recognition datasets, which inspired us to use transfer learning to transfer knowledge from ship detection to ship recognition. In this paper, a method for ship recognition based on transfer learning that utilizes the knowledge gained from the ship detection task is proposed. The method includes two modules: pretraining module and fine-tuning module. It can apply samples of unlabeled ship types to ship recognition, thus reducing the number of labeled samples that are required for ship recognition. The experimental results on GPED and MTDD show that our method can achieve good recognition performances.

Retrieving the displacements of the Hutubi (China) underground gas storage during 2003–2020 from multi-track InSAR

Many countries and regions build underground gas storages (UGSs) to regulate the energy demand and supply in different seasons. The Hutubi underground gas storage (HUGS) in Xinjiang province is the largest UGS in China, so it is of great significance to monitor its operation. At present, the researches on the HUGS mainly rely on traditional geodetic monitoring (e.g., in-situ leveling and global navigation satellite system (GNSS)), which has a short time span and low spatial resolution. The whole-domain and long-temporal sequence surface displacements induced by gas recovery (before 2013) and injection/extraction (since 09/06/2013) were seldom reported. In this study, the large-scale background deformation was firstly obtained using the ALOS PALSAR data (2006–2011), and the displacement time series of the HUGS over 2003–2020 was observed by all available Synthetic Aperture Radar (SAR) data from multiple SAR sensors (Envisat ASAR, TerraSAR/TanDEM-X, Sentinel-1). The results show that this area had a long history of slow subsidence (2.2 mm/yr) before 2013. Since 06/2013, the surface of the HUGS showed periodic uplift and subsidence with a net uplift. The average uplift rate in the center was about 7 mm/yr during 08/2013–07/2015 and 13 mm/yr during 03/2015–05/2020. The deformation has a temporal correspondence with the gas extraction/injection process. The accuracy is assessed by the cross-validation of the results of different datasets and the GNSS measurements. The compound dislocation model (CDM) is used to model the dynamic displacements caused by gas injection/extraction in HUGS. This model can reflect the UGS volume changes and the estimated central depth (3499.7 m) and height (108.6 m) of the storage are consistent with the actual central depth (~3585 m) and height (~110 m). We also restore the dynamic change of the pore pressure and the injected gas volume during the 5–7 cycles, on the basis of the linear relations between gas injection volume, pore pressure, and CDM parameters. In 12/2017, the estimated gas inventory of the HUGS is 91.29 × 108 m3, about 4.7% smaller than the real gas inventory of 95.77 × 108 m3. At the end of the seventh injection/extraction cycle, the predicted maximum gas injection volume and pore pressure reached 11.2 billion m3 and 36 MPa, respectively.

Monitoring surface deformations of the reclamation site using PS interferometry: Senol Gunes sport complex (Turkey)

Recently, structures, built on reclamation sites in the Eastern Black Sea region are quite remarkable. As one of them, Şenol Güneş Medical Park Arena Sport Complex, which is located in Akyazı town of Trabzon city, is one of the rare areas in Turkey. It is very important to monitor surface deformations on this type of unfamiliar and very expensive constructions for protecting human life and property and for taking necessary precautions in advance. Persistent Scatterer Interferometry (PSI), based on the analysis of time series of multiple Synthetic Aperture Radar (SAR) data, is an invaluable geodetic tool to measure surface deformations. So, in this study, it is aimed to monitor the surface deformations on the reclamation site between the dates 20/12/2016–28/01/2020 by StaMPS (Stanford Method for Persistent Scatterers)-PSI technique using 96 Sentinel-1 SAR images. For processing, integration of European Space Agency (ESA) SentiNel Application Platform (SNAP) and StaMPS programs are used. After processing, surface deformations are determined as annual velocities (millimeter(mm)/year(y)) of PS points in the reclamation area along the line-of-sight (LOS) direction between the values −9.7 mm/y and 3.9 mm/y with an average standard deviation of 0.7 mm/y. From the results, it is understood that the area demonstrates very heterogeneous deformations rates, which occur on both horizontal and vertical directions because of the diversity and being on the reclamation site of the structures.

A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.

Endfire Synthetic Aperture Radar for a Cryobot for Exploration of Icy Moons and Terrestrial Glaciers

An endfire synthetic aperture radar (SAR) for use on a cylindrical ice-penetrating cryobot is presented. The SAR facilitates obstacle detection and mapping inside ice for subsurface exploration using such a cryobot vehicle. The SAR is comprised of four azimuthally arranged directional log periodic antenna elements flush-mounted onto the cryobot’s surface. Aperture synthesis is facilitated by the downward trajectory of the cryobot as it slowly melts through ice. The radar front end and back end are designed using commercial-off-the-shelf (COTS) components and a customized field-programmable gate array (FPGA)-based digital design for signal generation, reception, and processing. Theoretical analysis of the SAR geometry is presented for the case of a point target in which the maximum likelihood estimation (MLE) approach is used for position estimator development. The novelty of this system lies in the use of pairs of antennas, with fixed baselines between them, to implement multiple coherent monostatic SARs to estimate target position in three dimensions. Specifically, range estimation is implemented by conventional chirp processing, polar (or elevation) angle estimation is achieved by SAR processing, and azimuth angle estimation is facilitated using pairs of four azimuthally arranged antennas that can be analyzed as multiple fixed baseline interferometric radars. Radar signal coherency tests and experimental validation of point target estimation and spatial resolution performed in a laboratory environment using the endfire SAR are presented in this study.

Evaluating P-Band TomoSAR for Biomass Retrieval in Boreal Forest

P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden. BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%–36% (27–33 t/ha) for airborne data and 38%–39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity.

Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite

Synthetic aperture radar (SAR) can extract sea surface wind speed information. To extract wind speed information through the geophysical model function (GMF), the corresponding wind direction information must be input. This article introduces some concepts about networked SAR satellites. The networked satellites enable multiple SARs to observe the same sea surface at different incidence angles at the same time. Aiming at the X-band networked SAR data with different incident angles, the cost function is established by using the GMF. By minimizing the cost function, accurate wind speed information can be extracted without inputting wind direction information. When the noise is small, the wind direction information is introduced, and the accuracy of the extracted wind speed will be improved. When the noise is less than 1 dB and the incident angle is greater than 30°, the root-mean-square error (RMSE) of the wind speed extracted by this method is basically less than 2 m/s.