SAR Raw Data Research Articles

Locating RFI Source by Integrating Ascending and Descending Passes Based on SAR Raw Data

Locating RFI Source by Integrating Ascending and Descending Passes Based on SAR Raw Data

Composite Indicator for Detecting and Localizing Time-Varying RFI in SAR Raw Data

Composite Indicator for Detecting and Localizing Time-Varying RFI in SAR Raw Data

AN INTEGRATED METHOD FOR REFOCUSING OF MOVING TARGETS IN SPOTLIGHT SAR

A new target-refocusing technique based-on re-centering phase computation of previously recorded moving target raw data is implemented to the Spotlight SAR data in order to obtain refocused moving targets. The technique is tested on the integrated simulated data; background real spotlight SAR Raw data with the synthetically generated data domes of civilian moving targets. First Polar format Algorithm is applied to detect and estimate the speed of ground-moving targets on the integrated raw data. At the next step, re-organize the integrated raw data by selecting and arranging target focusing center with a new technique based on re-centering phase computation to each moving target speed. At the third step re-organize the raw data by re-centering the phase computation to each moving target location. Finally, Polar Format Algorithm is applied to each reorganized raw data to obtain highly focused moving targets individually

A Monostatic Forward-Looking Staring Spotlight SAR Raw Data Generation and Hybrid-Domain Image Formation Modifications Based on Extended Azimuth Nonlinear Chirp Scaling Autofocusing

Forward-looking staring spotlight synthetic aperture radar (FSS-SAR) offers operational advantages over conventional squinted SAR counterparts. However, due to its complex observation configuration, pixel anomalies occur when processing raw data. For instance, in the forward-looking geometry, symmetrically distributed point scatterers along the flight path have the same Doppler history, resulting in azimuth ambiguities, whereas staring observation narrows and skews the 2D received spectrum, reducing signal orthogonality and causing spectral folding. As a result, not only is the Doppler frequency gradient too small to be precisely measured in the FSS-SAR mode, limiting the ability to achieve high azimuth angular resolution, but it also suffers from azimuth ambiguities when forming an image. To overcome such azimuth-variant characteristics known as inherent resolutional anomalies, a hybrid-domain image formation algorithm (IFA) based on azimuth scaling is modified here. As an approach, radiometric bias correction (RBC) techniques in conjunction with an extended azimuth nonlinear chirp scaling (ANCS) algorithm capable of equalizing range space variance in the azimuth direction, which benefits from an embedded autofocusing step, have been designed for raw data processing. Given the fact that the platform experiences trajectory deviations under a real-flight condition compared to an ideal trajectory, a pre-processing motion compensation (MOCO) step based on Kalman filtering is included within the IFA. The entire proposed IFA solution tackled the inherent resolutional anomalies of FSS-SAR and succeeded in reconstructing the focused image without azimuthal anomalies. To further assess the IFA, a modified Cartesian back-projection (BP) algorithm was developed along with other objective evaluation scenarios, all of which confirm that the proposed hybrid-domain extended ANCS algorithm is valid for the FSS-SAR application.

A Shadow Simulation Scheme for SAR Images of Undulating Terrain Based on Facet Cell Fitting and Elevation Angle Comparison

Shadow simulation is a key part of SAR raw data and image simulation for undulated terrain, which will directly determine the accuracy of SAR simulation and will also affect the accuracy of subsequent shadow-based SAR image processing applications. In this paper, a shadow simulation scheme for undulated terrain SAR images based on facet cell fitting and elevation angle comparison is proposed. In the scheme, the height information of each grid point on the same line of sight within the beam irradiation range is obtained firstly with the facet cell fitting, and the elevation angle of each grid point can be computed and compared to determine whether the occlusion happens. Then, all grid points in the illumination range are traversed to obtain the shadow judgment result at a certain azimuth moment, and the SAR shadows are obtained by superimposing the judgment results at all azimuth moments. At last, simulated and measured data experiments are given to verify the effectiveness of the proposed scheme, and the results show that this scheme can accurately simulate the SAR shadow of undulating terrain.

Full-Aperture Processing of Airborne Microwave Photonic SAR Raw Data

Full-Aperture Processing of Airborne Microwave Photonic SAR Raw Data

Performance-Optimized Quantization for SAR and InSAR Applications

For the design of present and next-generation spaceborne SAR missions, constantly increasing data rates are being demanded, which impose stringent requirements in terms of onboard memory and downlink capacity. In this scenario, the efficient quantization of SAR raw data is of primary importance, since the utilized compression rate is directly related to the volume of data to be stored and transmitted to the ground and, at the same time, it affects the resulting SAR imaging performance. In this paper, we introduce the performance-optimized block-adaptive quantization (PO-BAQ), a novel approach for SAR raw data compression which aims at optimizing the resource allocation and, at the same time, the quality of the resulting SAR and InSAR products. This goal is achieved by exploiting the a priori knowledge of the local SAR backscatter statistics, which allows for the generation of high-resolution bitrate maps that can be employed to fulfill a predefined performance requirement. Analyses on experimental TanDEM-X interferometric data are presented, which demonstrate the potentials of the proposed method as a helpful tool for performance budget definition and data rate optimization of present and future SAR missions.

Azimuth Interrupted FMCW SAR for High-Resolution Imaging

Azimuth interrupted frequency modulation continuous-wave synthetic aperture radar (AI-FMCW SAR) is a novel type of light and miniaturized sensor that works on satellites, which can transmit and receive signals with a single antenna. Different from conventional pulse SAR, the instantaneous duty cycle of pulse is close to 100%. Several pulses could not be transmitted and would be replaced by receiving echoes. There will be gaps in the received echoes. It is necessary to reconstruct the gapped data; otherwise, there will be ghosting with traditional imaging algorithms. The current algorithms for reconstructing the gapped data are computationally time-consuming and not suitable for high-resolution imaging. Therefore, a new imaging algorithm is proposed to focus on the AI-FMCW SAR raw data via subaperture processing, which is called subaperature sparse reconstructing technique (SSRT). It can significantly suppress ghost targets and has a high calculation speed. In this letter, the spaceborne AI-FMCW SAR signal model was proposed first. Then, SSRT was proposed to process the AI-FMCW SAR data, in which the generalized orthogonal matching pursuit (GOMP) algorithm was used to reconstruct the signals. Finally, the simulation experiments verified the effectiveness and rapidity of the proposed approach.

Geosynchronous SAR raw data simulator in presence of ionospheric scintillation using reverse backprojection

The future L-band geosynchronous synthetic aperture radar (GEO SAR) system will be inevitably affected by ionospheric scintillation. Due to the lack of the real system, it is necessary to achieve the scintillation-affected GEO SAR raw data for the research of influential mechanism and correction strategies. A method for the generation of scintillation-affected GEO SAR raw data is proposed that utilises the reverse backprojection (ReBP) to efficiently and accurately modulate the scintillation transfer function on GEO SAR signals. A simulation experiment for a real SAR scene from one of the ALOS-PALSAR observation is operated in case of the current inclined L-band GEO SAR system configuration. The simulation takes less time by using the ReBP, compared with the existent method, and shows the speckle-like phase error similar to the injected scintillation phase error, which validates the nice efficiency and accuracy of the proposed methodology.

Efficient Strip-Mode SAR Raw-Data Simulator of Extended Scenes Included Moving Targets Based on Reversion of Series

Efficient Strip-Mode SAR Raw-Data Simulator of Extended Scenes Included Moving Targets Based on Reversion of Series

Sparse Scene Recovery for High-Resolution Automobile FMCW SAR via Scaled Compressed Sensing

This paper introduces a sparse scene reconstruction algorithm for automobile frequency-modulated continuous-wave synthetic aperture radar (FMCW SAR) through scaled compressed sensing (CS). An FMCW radar leads to low manufacturing cost, compact realization, and low transmission power. An automobile SAR is more economical and easier to implement than typical SAR platforms (e.g., satellites and aircraft). We apply CS to randomly subsampled raw data of automobile FMCW SAR for sparse reconstruction. We exploit the fact that the velocity of an automobile is significantly lower than that of the SAR platforms, which leads to a vastly narrow bandwidth of an azimuth-matched filter in the azimuth compression of the range-Doppler algorithm (RDA). Low-frequency azimuth data have a fundamental effect on azimuth compression. We propose a new reconstruction scheme, scaled CS, which specializes in low-frequency information recovery for automobile SAR. The scheme is based on basis pursuit denoising (BPDN). A Ku-band FMCW SAR system is developed to verify the performance of the proposed algorithm. We mount our system on an automobile and collect FMCW SAR raw data in the stripmap mode with a van maintained a constant speed on a highway. The proposed reconstruction algorithm shows improved recovery performance for automobile FMCW SAR, which is validated by processing a high-resolution real SAR image.

Sparse Manifold-Regularized Neural Networks for Polarimetric SAR Terrain Classification.

In this article, a new deep neural network based on sparse filtering and manifold regularization (DSMR) is proposed for feature extraction and classification of polarimetric synthetic aperture radar (PolSAR) data. DSMR uses a novel deep neural network (DNN) to automatically learn features from raw SAR data. During preprocessing, the spatial information between pixels on PolSAR images is exploited to weight each data sample. Then, in the pretraining and fine-tuning, DSMR uses the population sparsity and the lifetime sparsity (dual sparsity) to learn the global features and preserves the local structure of data by neighborhood-based manifold regularization. The dual sparsity only needs to tune a few parameters, and the manifold regularization cuts down the number of training samples. Experimental results on synthesized and real PolSAR data sets from different SAR systems show that DSMR can improve classification accuracy compared with conventional DNNs, even for data sets with a large angle of incidence.

SAR high‐resolution imaging from missing raw data using StOMP

SAR raw data missing occurs when radar is interrupted for various reasons. In order to deal with this problem, a method is proposed to focus the missing SAR raw data via stage-wise orthogonal matching pursuit (StOMP). A reference function in time domain is designed for the missing raw data. After matched with the reference function, the energy of missing raw data is concentrated in two-dimensional frequency domain. Then, StOMP algorithm is available to recover the matched raw data in two-dimensional frequency domain. The recovered raw data is available to be processed with traditional SAR imaging algorithms. The Omega-K algorithm is chosen to deal with the recovered raw data in experiments. Point target and area target simulations are carried out to validate the effectiveness of the proposed method for azimuth missing SAR raw data. The data missing rate is 50% in both point and area target simulation. The resolution of point targets can reach 0.3 m in both range and azimuth direction.

Improved BAQ algorithm for Tiangong‐2 interferometric imaging radar data compression

Tiangong-2 interferometric imaging radar altimeter (InIRA), the first space-borne radar altimeter in the world, applying a new mechanism of interferometry with short baseline and small incident angle, can achieve wide swath and high accuracy sea surface height measuring. Comparing to traditional altimeter, InIRA can obtain SAR image of observation area, which means InIRA needs higher AD sampling rate. Due to the limitation of satellite on-board storage and downlink speed, in order to extend the working time of InIRA, data compression algorithm for InIRA must be studied. Block adaptive quantisation (BAQ) is an efficient and widely adopted approach for space-borne SAR raw data compression. Based on the characters of InIRA echo waveform, this study discusses compressing InIRA raw data by changing quantisation bits and size of blocks of BAQ algorithm to achieve higher compression ratio and less sea surface height error.

SAR raw data simulation for multiple‐input multiple‐output video synthetic aperture radar using beat frequency division frequency modulated continuous wave

AbstractSynthetic aperture radar (SAR) raw data simulation, operating under various scenarios, and realistic environments, are necessary for analyzing and verifying the performance aspects of SAR systems and the various algorithms used in them. In this article, we propose a method to simulate the raw data generated by multiple‐input multiple‐output video synthetic aperture radar (MIMO ViSAR) from a speckled scene, using beat frequency division (BFD) frequency‐modulated continuous wave (FMCW). Using an optical image as a reflectivity map for a realistic scene, we first multiply a speckle noise with this image to generate a speckled reflectivity map. We then compute the two‐dimensional convolutions of a SAR point spread function of a MIMO ViSAR system with the speckled reflectivity map to generate a complex SAR image. Finally, the SAR raw data is generated through inverse processing of the MIMO ViSAR system. We use the synthesized raw data to reconstruct SAR images, based on our previously proposed MIMO ViSAR signal‐processing algorithm, to demonstrate and verify the effectiveness of the proposed method.

An Improved BAQ Encoding and Decoding Method for Improving the Quantized SNR of SAR Raw Data.

When the original echo data of SAR are saturated for quantization, the performance of the commonly used block adaptive quantization (BAQ) algorithm will be degraded, which will degrade the imaging quality. This article proposes an improved Llody-Max codec method, which only needs to change the codec look-up table to get better quantization performance when the original echo is saturated. The simulation results show that the proposed method can reduce the quantization power loss, improve the echo signal-to-noise ratio (SNR), and reduce the influence of quantization saturation on the scattering mechanism of polarized SAR data, which have good practical application value.

실제 SAR 영상에서의 최소 엔트로피 기반의 자동 초점 기법 연구

In cases of airborne equipped with SAR, because the occurrence of motion is inevitable, it is necessary to apply autofocus techniques to SAR images to improve the image performance degradations caused by residual errors. Herein, a robust autofocus algorithm based on the minimum entropy criteria is proposed for the real SAR data in the spotlight mode. The convergence condition of the phase error estimation is checked at every iteration and if it is violated, the size of the phase error estimation is adjusted to the convergence condition. The real SAR raw data is used to demonstrate the excellent performance of the proposed algorithm.

An efficient raw data simulation algorithm for large complex marine targets and extended sea clutter in spotlight SAR

AbstractAn efficient algorithm is proposed to simulate the backscattering field of a complex marine target and surrounding 3D sea surface as clutter. Sea surface backscattering is simulated based on the numerical solution of the Electric Field Integral Equation by an improved meshing algorithm. The complex target is modeled using the discrete scattering centers independently. Combining the target and sea surface backscattered fields is used to extract the SAR raw data. The effectiveness of the proposed algorithm is verified for 2 hypothetical cases.

Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery

The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82–0.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8–28.5 Mg ha−1. Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery.

스포트라이트 모드 SAR 영상 형성에서의 수정된 가중치 최소 자승기법에 의한 자동 초점 알고리즘

In the existence of motion, azimuth phase error due to accuracy limitation of GPS/IMU and system delay is unavoidable and it is essential to apply autofocus to estimate and compensate the azimuth phase error. In this paper, autofocus algorithm using MWLS(Modified WLS) is proposed. It shows the robust performance compared with original WLS using new target selection/sorting metric and iterative azimuth phase estimation technique. SAR raw data obtained in a captive flight test is used to validate the performance of the proposed algorithm.