Synthetic Aperture Radar Signal Research Articles

Intricate multiple scattering features of artificial facilities in X-Band SAR images

Abstract. Due to the intricate distortion and reflection geometry of the SAR signal, it is typically difficult to determine the multiple scattering of large artificial objects in SAR images. This work presents a scattering point path tracking model that utilizes the real three-dimensional dimensions of targets, based on the geometric optics method. Three different artificial structures, including light poles, cable stayed bridges, and power transmission lines, are carefully analysed in time-series SAR images with their simulated multiple scattering results. The results demonstrate that the routes determined by the model are consistent with the multiple scattering features on SAR images. Moreover, the time-series data demonstrate that ripples in the water's surface have a significant impact on the multi-scattering features of power lines and bridges. The double scattering features of the light pole provides a novel approach to the process of permanent scatterers (PS) in urban areas. The instances presented in this study demonstrate the effectiveness of the scattering point path tracking model in identifying the various artificial facility targets on different reflective surfaces. It will be a useful tool for deciphering the multiple scattering of large artificial structures when their 3D model is known.

DUAL-POLARIMETRIC DECOMPOSITION OF SENTINEL-1 SAR IMAGE AND MACHINE LEARNING MODEL FOR OIL SPILL DETECTION: CASE OF MINDORO OIL SPILL

Abstract. Oil spills represent a significant environmental hazard necessitating timely detection to mitigate their detrimental effects. Synthetic Aperture Radar (SAR) technology serves as a remote sensing (RS)-based tool capable of detecting oil spills under varying weather conditions and at all times of day. SAR polarimetry, which assesses the polarization of the backscattered SAR signal, can effectively discriminate oil spills from other features that may manifest as dark regions in the SAR images. The integration of machine learning algorithms offers significant potential for enhancing the accuracy and efficiency of oil spill detection through SAR polarimetry. In recent years, several studies have introduced machine learning-based methodologies for this purpose, yet a comprehensive evaluation of their real-world performance remains essential. This study aimed to assess the efficacy of a machine learning (ML)-based approach for oil spill detection utilizing features derived from a dual-polarimetric decomposition method applied to Sentinel-1 SAR data. Results show that the machine learning-based approach achieved notable accuracy in oil spill detection reaching a score of 0.569 for intersection over union and 72.50 for f1-score of oil spill areas. Overall, this research underscores the potential of ML techniques as valuable tools for oil spill detection via SAR polarimetry.

A Fast Factorized Back-Projection Algorithm Based on Range Block Division for Stripmap SAR

Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm’s efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining its computational efficiency. However, the above method is only operated in the azimuth direction, and the computing efficiency needs to be urgently improved in the actual processing process. This paper proposes a fast factorized back-projection algorithm for stripmap SAR imaging based on range block division. The echo data are divided into multiple subblocks in the range direction, and FFBP processing is applied separately to each full-aperture subblock, further enhancing computational efficiency. The paper analyzes the algorithm’s principles, underscores the necessity of integral aperture determination and full-aperture data block processing, provides specific implementation steps, and applies the algorithm to point target simulation and experimental data from a vehicle-mounted ice radar. The experiments validate the algorithm’s efficiency in stripmap SAR imaging.

CTV-Net: Complex-Valued TV-Driven Network With Nested Topology for 3-D SAR Imaging.

The regularization-based approaches offer promise in improving synthetic aperture radar (SAR) imaging quality while reducing system complexity. However, the widely applied l1 regularization model is hindered by their hypothesis of inherent sparsity, causing unreal estimations of surface-like targets. Inspired by the edge-preserving property of total variation (TV), we propose a new complex-valued TV (CTV)-driven interpretable neural network with nested topology, i.e., CTV-Net, for 3-D SAR imaging. In our scheme, based on the 2-D holography imaging operator, the CTV-driven optimization model is constructed to pursue precise estimations in weakly sparse scenarios. Subsequently, a nested algorithmic framework, i.e., complex-valued TV-driven fast iterative shrinkage thresholding (CTV-FIST), is derived from the theory of proximal gradient descent (PGD) and FIST algorithm, theoretically supporting the design of CTV-Net. In CTV-Net, the trainable weights are layer-varied and functionally relevant to the hyperparameters of CTV-FIST, which aims to constrain the algorithmic parameters to update in a well-conditioned tendency. All weights are learned by end-to-end training based on a two-term cost function, which bounds the measurement fidelity and TV norm simultaneously. Under the guidance of the SAR signal model, a reasonably sized training set is generated, by randomly selecting reference images from the MNIST set and consequently synthesizing complex-valued label signals. Finally, the methodology is validated, numerically and visually, by extensive SAR simulations and real-measured experiments, and the results demonstrate the viability and efficiency of the proposed CTV-Net in the cases of recovering 3-D SAR images from incomplete echoes.

Synthetic aperture radar (SAR) detects large gas seeps in Alaska lakes

Reservoirs of 14C-depleted methane (CH4), a potent greenhouse gas, residing beneath permafrost are vulnerable to escape where permafrost thaw creates open-talik conduits. However, little is known about the magnitude and variability of this methane source or its response to climate change. Remote-sensing detection of large gas seeps would be useful for establishing a baseline understanding of sub-permafrost methane seepage, as well as for monitoring these seeps over time. Here we explored synthetic aperture radar’s (SAR) response to large sub-permafrost gas seeps in an interior Alaskan lake. In SAR scenes from 1992 to 2011, we observed high perennial SAR L-band backscatter (σ 0) from a ∼90 m-wide feature in the winter ice of interior Alaska’s North Blair Lake (NBL). Spring and fall optical imagery showed holes in the ice at the same location as the SAR anomaly. Through field work we (1) confirmed gas bubbling at this location from a large pockmark in the lakebed, (2) measured flux at the location of densest bubbles (1713 ± 290 mg CH4 m−2 d−1), and (3) determined the bubbles’ methane mixing ratio (6.6%), radiocarbon age (18 470 ± 50 years BP), and δ13CCH4 values (−44.5 ± 0.1‰), which together may represent a mixture of sources and processes. We performed a first order comparison of SAR σ 0 from the NBL seep and other known sub-permafrost methane seeps with diverse ice/water interface shapes in order to evaluate the variability of SAR signals from a variety of seep types. Results from single-polarized intensity and polarimetric L-band SAR decompositions as well as dual-polarized C-band SAR are presented with the aim to find the optimal SAR imaging parameters to detect large methane seeps in frozen lakes. Our study indicates the potential for SAR remote sensing to be used to detect and monitor large, sub-permafrost gas seeps in Arctic and sub-Arctic lakes.

A Feasibility Analysis of the Use of ISAR Training Data in Machine Learning-Based SAR ATR

Processing of synthetic aperture radar (SAR) images for automatic target recognition (ATR) is a critical application especially in military surveillance. In particular, numerous machine learning-based SAR ATR methods have been proposed for this task. However, data training and testing stages of all these methods are based on the exploitation of SAR signatures of the target under investigation. Considering the high variability of radar targets, obtaining such signature data is obviously a costly and time consuming process. In this study, therefore, a feasibility analysis of the use of inverse-SAR (ISAR) training data in SAR ATR has been made for the first time. The turntable ISAR and circular SAR images of three different vehicles are used in training and testing is performed by means of SAR images of three similar targets within the publicly available MSTAR dataset. Also, three most prominent machine learning methods, namely KNN, SVM and ANN are used in conjunction with three different feature extraction algorithms namely, GLRLM, GLSZM and GLCM. The obtained results reveal that the GLCM+SVM algorithm pair is the most effective model with 85% accuracy.

A Novel Chaotic-NLFM Signal under Low Oversampling Factors for Deception Jamming Suppression

Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed.

Ionospheric compensation in L-band InSAR time-series: Performance evaluation for slow deformation contexts in equatorial regions

Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) is the only geodetic technique allowing to measure ground deformation down to mm/yr over continuous areas. Vegetation cover in equatorial regions favors the use of L-band SAR data to improve interferometric coherence. However, the electron content of ionosphere, affecting the propagation of the SAR signal, shows particularly strong spatio-temporal variations near the equator, while the dispersive nature of the ionosphere makes its effect stronger on low-frequencies, such as L-band signals. To tackle this problem, range split-spectrum method can be implemented to compensate the ionospheric phase contribution. Here, we apply this technique for time-series of ALOS-PALSAR data, and propose optimizations for low-coherence areas. To evaluate the efficiency of this method to retrieve subtle deformation rates in equatorial regions, we compute time-series using four ALOS-PALSAR datasets in contexts of low to medium coherence, showing slow deformation rates (mm/yr to cm/yr). The processed tracks are located in Ecuador, Trinidad and Sumatra, and feature 15 to 19 acquisitions including very high, dominating ionospheric noise, corresponding to equivalent displacements of up to 2 m. The correction method performs well and allows to reduce drastically the noise level due to ionosphere, with significant improvement compared with a simple plane fitting method. This is due to frequent highly non-linear patterns of perturbation, characterizing equatorial TEC distribution. We use semivariograms to quantify the uncertainty of the corrected time-series, highlighting its dependence on spatial distance. Thus, using ALOS-PALSAR-like archive, one can expect a detection threshold on the Line-of-Sight velocity ranging between 3 and 6 mm/yr, depending on the spatial wavelength of the signal to be observed. These values are consistent with the accuracy derived from the comparison of velocities between two tracks in their overlapping area. In the case studies that we processed, the time-series corrected from ionosphere allows to retrieve accurately fault creep and volcanic signal but it is still too noisy for retrieving tiny long-wavelength signals such as slow (mm/yr) interseismic strain accumulation.

Enhanced range-doppler algorithm for SAR image formation

A Synthetic Aperture Radar (SAR) system is a powerful source of information due to its capability to operate day and night and in all weather conditions. It is essential for military and civilian purposes. Pulse-focusing is employed by the SAR system for achieving both long-range detection and fine-range resolution. The Range-Doppler Algorithm (RDA) is the most popular pulse-focusing technique for creating images from coherent SAR data. Pulse compression techniques for SAR signal processing targets seek to improve map resolution, lower peak power, and increase the Signal-to-Noise Ratio (SNR) of the detected object. This paper demonstrates an enhanced RDA algorithm that uses a modified pulse compression technique based on mismatched filter optimization where Quadratic Constrained Quadratic Program (QCQP) is used. The performance evaluation of the proposed method is performed using visual assessment and the standard image quality metrics peak-side-lobe-ratio (PSLR), impulse-response-width (IRW), and integrated-side-lobe-ratio (ISLR). Simulated and real SAR raw data are used for performance evaluation. The experimental results demonstrate the proposed method’s robustness, efficiency, and reliability in providing a higher-quality SAR image than the traditional RDA algorithm.

Enabling High-Resolution Micro-Vibration Detection Using Ground-Based Synthetic Aperture Radar: A Case Study for Pipeline Monitoring

The wellbeing of pipelines is influenced by a range of factors, such as internal and external pressures, as well as deterioration over time due to issues like erosion and corrosion. It is thus essential to establish a reliable monitoring system that can precisely examine pipeline behavior over time in order to prevent potential damages. To this end, pipelines are inspected based on internal and external approaches. Radar, as a non-contact sensing system, can be a suitable choice for external pipeline inspection. Radar is capable of the transmission and receiving of thousands of signals in a second, which reconstructs the displacement signal and is used for a vibration analysis. Synthetic aperture radar (SAR) imaging adds cross-range resolution to radar signals. However, a data acquisition rate of longer than several seconds makes it unsuitable for sub-second vibration monitoring. This study aims to address this limitation by presenting a method for high-resolution vibration monitoring using ground-based SAR (GBSAR) signals. To this end, a signal processing method by modifying the radar’s signal model is presented, which allows for estimating scattering targets’ vibration parameters and angle of arrival with high resolution. The proposed method is validated with numerical simulation and a real case study comprising water pipelines. Moreover, various analyses are presented for the in-depth evaluation of the method’s performance in different situations. The results indicate that the proposed method can be effective in detecting pipeline vibration frequencies with micro-scale amplitudes while providing high spatial resolution for generating accurate vibration maps of pipelines. Also, the comparison with the radar observations shows a high degree of agreement between the frequency responses with the maximum error of 0.25 Hz in some rare instances.

Relationship between Lidar-Derived Canopy Densities and the Scattering Phase Center of High-Resolution TanDEM-X Data

The estimation of forestry parameters is essential to understanding the three-dimensional structure of forests. In this respect, the potential of X-band synthetic aperture radar (SAR) has been recognized for years. Many studies have been conducted on deriving tree heights with SAR data, but few have paid attention to the effects of the canopy structure. Canopy density plays an important role since it provides information about the vertical distribution of dominant scatterers in the forest. In this study, the position of the scattering phase center (SPC) of interferometric X-band SAR data is investigated with regard to the densest vegetation layer in a deciduous and coniferous forest in Germany by applying a canopy density index from high-resolution airborne laser scanning data. Two different methods defining the densest layer are introduced and compared with the position of the TanDEM-X SPC. The results indicate that the position of the SPC often coincides with the densest layer, with mean differences ranging from −1.6 m to +0.7 m in the deciduous forest and +1.9 m in the coniferous forest. Regarding relative tree heights, the SAR signal on average penetrates up to 15% (3.4 m) of the average tree height in the coniferous forest. In the deciduous forest, the difference increases to 18% (6.2 m) during summer and 24% (8.2 m) during winter. These findings highlight the importance of considering not only tree height but also canopy density when delineating SAR-based forest heights. The vertical structure of the canopy influences the position of the SPC, and incorporating canopy density can improve the accuracy of SAR-derived forest height estimations.

Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data

Accurate and timely monitoring of flooded crop areas is crucial for disaster rescue and loss assessment. However, most flooded crop monitoring methods based on synthetic aperture radar (SAR) imagery were developed for rice, which is probably inappropriate for crops with complex canopy structures that strongly attenuate SAR signals. Additionally, these methods often rely on empirical thresholds and region-specific reference samples, limiting their reliability and applicability on a larger spatial scale. To address these issues, we developed a novel flooded crop mapping approach at a regional scale using Sentinel-1 time-series data and an unsupervised Gaussian Mixture Model (GMM). Our approach leverages a Flood Separability Index (FSI) derived from the fitted probability density function of flooded and non-flooded crop areas in a GMM. This allows us to overcome the limitations of manual input selection in previous studies. The multi-temporal GMM was constructed using the time-series images with optimal polarization to estimate the flooded crop extents on a regional scale. We also investigated the scattering mechanisms of three typical crop disaster structures within an agricultural landscape area. Our results indicate that the proposed multi-temporal GMM is robust in crop planting areas with complex canopy structures. The performance of both single-temporal and multi-temporal GMMs surpasses that of baseline methods such as Otsu and K-means. Compared with VV polarization, VH polarization exhibits greater potential for accurately mapping flooded crops in complex agricultural regions. Our approach does not require labeled samples or many predefined parameters, making it fast and feasible for mapping flooded crops with complex canopy structures in large spatial areas.

A novel approach to detect the spring corn phenology using layered strategy

Accurate and continuous crop phenology information at a regional scale is important for agronomic management and yield estimation. However, detecting continuous crop phenology remains challenging due to the low sensitivity of remote sensing signals to certain phenological stages and the limited of availability remote sensing images. Therefore, this study developed a layered strategy to detect continuous crop phenology. First, a novel Phenology Separability Index (PSI) is established to select features from the Gaussian probability density distribution. PSI quantifies the capability of optical vegetation indexes (VIs), Synthetic Aperture Radar (SAR) signals, and meteorological factors to distinguish between various phenological stages. Then, the multi-temporal sample is established to enhance training sample representation and quantity. Finally, a random forest model is trained using features extracted from multi-temporal samples to improve detection accuracy. This model effectively reduces phenological stage confusion due to redundant features and limited samples. In addition, this study validated its extensibility by mapping crop phenology in the cities of Acheng, Zhaozhou, Lishu, and Buxin and assessed its uncertainty using the Sobol approach. Results indicated that growing degree day has the highest separability among meteorological factors, surpassing both SAR singles and optical VIs. Moreover, the proposed layered strategy was robust, explaining 96% of spatial variation in crop phenology at the regional scale. The accuracy of the layered strategy method (total RMSE = 8.74 days) surpassed that of the multi-temporal sample method (total RMSE = 15.76 days) and the traditional method with a single-temporal sample (total RMSE = 17.21 days). In addition, this study indicated that optical VIs are prone to confuse with the early or late phenological stage of corn, whereas SAR singles are highly sensitive to jointing date.

ML based approach for inverting penetration depth of SAR signals over large desert areas

Penetration depth of synthetic aperture radar (SAR) signals over a desert is a key parameter to understand the internal properties of the desert. Existing approaches for obtaining the penetration depth require good quality interferometric SAR (InSAR) data of very short temporal and long spatial baselines. Such data are often difficult to obtain in a highly dynamic desert. We propose a new machine learning (ML) based approach for inverting penetration depth of SAR signals over large desert areas by jointly using InSAR, polarimetric SAR (PolSAR) and optical remote sensing data. First, SAR scattering parameters and terrain properties are determined based on PolSAR and Landsat 5 TM multispectral data and a DEM. The penetration depth of SAR signals over a small desert area is obtained based on methods such as using a scattering model. A random forest model is then used to establish the relationship between the SAR scattering parameters and site features, and the penetration depth, and then is used to derive the penetration depth over a large desert area. The approach is applied to calculate the penetration depth of ALOS-1 PALSAR L-band signals for a large part of the Kufra Basin, an area of about 60, 000 km2. The penetration depths of four types of typical landforms in area (i.e., sandy plains, paleochannels, rocks and man-made features) are discussed in relation to the geological and climatic conditions. The average signal penetration depths over the paleochannels, sandy plains, and rocks and man-made features are 2.84 m, 1.97 m, 1.21 m, respectively. It is found that the backscattering coefficient, dielectric constant, surface roughness and mineral composition are the most important parameters in determining the signal penetration depths. An interesting point is that the existence of hematite in the sand can increase the dielectric dissipation of the sand medium and shorten the signal penetration depth.

Retrieving Soil Moisture at the Field Scale from Sentinel-1 Data over a Semi-Arid Mediterranean Agricultural Area

In this work, superficial soil moisture is estimated from SAR data at the field scale on agricultural fields over which the relationship between the co-polarized backscattering coefficient (γ0VV) and the measured soil moisture (SSMv) is both direct and inverse. An inversion algorithm is adapted to the charateristics of the single field and applied to SAR signal differences. The differences of SAR signal are obtained from a change detection (CD) method applied on the VV band of the Sentinel-1 SAR mission. In the CD method, the variations of the total backscattered signal due to sharp changes in vegetation and soil roughness are excluded from the dataset by using a machine learning algorithm. The retrieval method is applied on a low vegetated agricultural area in Spain, characterized by a semi-arid mediterranean climate and where in situ soil moisture data are available. Good results are obtained not only over fields characterized by direct γ0VV/SSMv relationship, reaching values of correlation coefficient and RMSE up to r=0.89 and RMSE=0.042 m3/m3, but also over fields with inverse relationship, obtaining in this case values up to r=0.84 ad RMSE=0.026 m3/m3. Although the inverse relationship between the backscattering coefficient and the measured soil moisture is not yet well understood in the field of soil moisture estimation from radar data, for the present case, checking the nature of this relationship was fundamental in order to accordingly adapt the soil moisture retrieval algorithm to the dataset characteristics.

An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images

Rice is one of the most important staple foods in the world, feeding more than 50% of the global population. However, rice is also a significant emitter of greenhouse gases and plays a role in global climate change. As a result, quickly and accurately obtaining the rice mapping is crucial for ensuring global food security and mitigating global warming. In this study, we proposed an automated rice mapping method called automated rice mapping using V-shaped phenological features of rice (Auto-RMVPF) based on the time-series Sentinel-1A images, which are composed of four main steps. First, the dynamic threshold method automatically extracts abundant rice samples by flooding signals. Second, the second-order difference method automatically extracts the phenological period of rice based on the scattering feature of rice samples. Then, the key “V” feature of the VH backscatter time series, which rises before and after rice transplanting due to flooding, is used for rice mapping. Finally, the farmland mask is extracted to avoid interference from non-farmland features on the rice map, and the median filter is applied to remove noise from the rice map and obtain the final spatial distribution of rice. The results show that the Auto-RMVPF method not only can automatically obtain abundant rice samples but also can extract the accurate phenological period of rice. At the same time, the accuracy of rice mapping is also satisfactory, with an overall accuracy is more than 95% and an F1 score of over 0.91. The overall accuracy of the Auto-RMVPF method is improved by 2.8–12.2% compared with support vector machine (SVM) with an overall accuracy of 89.9% (25 training samples) and 92.2% (124 training samples), random forest (RF) with an overall accuracy of 82.8% (25 training samples) and 88.3% (124 training samples), and automated rice mapping using synthetic aperture radar flooding signals (ARM-SARFS) with an overall accuracy of 89.9%. Altogether, these experimental results suggest that the Auto-RMVPF method has broad prospects for automatic rice mapping, especially for mountainous regions where ground samples are often not easily accessible.

Reservoir Ice Conditions from Multi-Sensor Remote Sensing and ERA5-Land: The Manicouagan Hydroelectric Reservoir Case Study

Reservoir ice can have an important impact on the watershed scale and influence hydraulic operations. On the other hand, hydropower generation can also impact the ice regime. In this study, multi-source satellite and ERA5-land data are used to evaluate ice conditions. Specifically, ice-controlling variables (temperature, water levels), ice regime (cover/deformation, thickness) and their interrelations are assessed for a 5-year period from 2017 to 2021. The methodology is applied to the Manicouagan reservoir, one of the largest hydropower reservoirs in Quebec, Canada. The satellite-based land surface temperatures (LSTs) suggest that winter 2021 was the hottest one. Overall, MODIS and Landsat LSTs agree with the ERA5-land temperatures. Ice backscatter from Sentinel-1 indicates that, in general, the reservoir is completely covered by ice from January to March. A correlation of 0.6 and 0.8 is observed between C- and Ku-band Synthetic Aperture Radar (SAR) signal and ice thickness, respectively. Important ice changes inferred from Differential Interferometric SAR (D-InSAR) occur approximately at the position where the largest ERA5-land ice thickness differences are observed. Winter water levels are also evaluated using satellite altimetric data to verify their influence on the ice dynamics. They show a decreasing tendency as the winter advances.

A novel block compressive sensing algorithm for SAR image formation

Compressive Sensing (CS) theory has been used for Synthetic Aperture Radar (SAR) imaging due to the sparsity feature of SAR images. Therefore, some well-known CS algorithms like Orthogonal Matching Pursuit (OMP) and Regularized OMP (ROMP) methods have been employed for SAR image formation with a very small number of samples. On the other hand, it has been shown that the SAR signal is consistent with the definition of block sparsity. Hence, compressive sensing methods employing block structure, known as Block Compressive Sensing (BCS), are presented and used for SAR image formation to achieve more accuracy with a smaller number of samples. In this paper, first, a new BCS-based algorithm, namely, Block Norm Regularized Orthogonal Matching Pursuit (BNROMP), is introduced which can be used in all BCS applications. Then, this novel method is used for SAR image formation to achieve more accuracy and excellent resolution with a small number of samples. The simulation results for the synthesized data, as well as real data, show that by using the novel BNROMP method, we could form SAR images with higher quality, as compared to those for the standard image formation algorithms and other CS-SAR or BCS-SAR methods.

SAR image formation scheme implementation and endorsement sprouting from Level-0 data decoding

Synthetic Aperture Radar (SAR) data processing evolving from level-0 raw data is complicated, especially in data decoding, manifesting in obtaining a well-focused SAR image. This paper is intended to present a complete MATLAB-based SAR data processing tool, which helps the end-user to treat simply the steps of image generation. This paper would enrich the research community in the field of SAR processors, especially in the area of understanding, handling, and developing a SAR processor based on space packet protocol standard (STD 01) used in many SAR systems such as Sentinel-1, ERS-1, CubeL, JPSS-2, 3, and 4. Also, this work opens the door for researchers to decode other space packet protocol standards and even to create an algorithm based on fully understanding the image formation algorithm from its roots. Moreover, the work in this paper could be a stepping-stone for the beginner in the field of SAR signal processing to become familiarized with SAR image generation procedures. The level-0 raw data used in this paper is based on Sentinel 1 SAR satellite obtained from the European Space Agency Copernicus website, a free open-source for level-0 and level-1 data types. The MATLAB program allows users to compare their generated image with the level-1 single-look complex (S1-L1-SLC) image utilizing entropy, contrast, and sharpness image quality metrics.The results showed that the images produced by the proposed algorithm are comparable to Sentinel-1 level-1 SAR images for the same scene and achieved satisfactory accuracy under the requirements for image quality.

Optimal estimation of Gaofen-3B satellite attitude deviation based on echo frequency domain features

This paper proposed and verified an optimal estimation method of satellite attitude deviation in orbit based on the frequency domain features of microwave remote sensing data from the Gaofen-3B (GF-3B) satellite. The Synthetic Aperture Radar (SAR) imaging results of the Amazon forest's strip pattern were obtained at different imaging incident angles of the GF-3B satellite. Then, the inversions of the Doppler center deviation of the SAR signal obtained the corresponding beam pointing errors at different imaging incident angles. Next, a three-degree-of-freedom nonlinear least square fitting model was established between the Doppler center deviation of the SAR signal and satellite attitude deviation. Combined with the measured SAR signals, satellite attitude deviations in different dimensions (pitch, yaw, and roll) were separated and estimated. On this basis, the GF-3B satellite proceeded with an on-orbit attitude correction test. The maximum Doppler center deviation at all imaging incident angles decreased from 400Hz to 46Hz, and the residual beam pointing errors were less than 0.01°. The proposed optimal estimation method can guarantee the on-orbit attitude deviation suppression and image quality improvement of the SAR satellites.