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

The real-time processing of synthetic aperture radar (SAR) data has a high requirement for the processor, which is a difficult problem in SAR real-time processing. With the rapid development of optoelectronic devices, traditional electrical SAR data processing can be converted into optoelectronic processing to improve the processing speed. In this paper, a new type of optical device is proposed to improve the processing speed of SAR data. With the help of a spatial light modulator (SLM), the initial SAR signal and matched filter function are loaded on the input plane and spectrum plane of the 4f system, respectively. Using an optical lens with the function of the Fourier transform, the Fourier transform and inverse Fourier transform of the SAR signal are carried out to realize the fast imaging of SAR. In theory, the processing speed of SAR data is the speed of light. Compared with traditional methods such as the range-Doppler (RD) algorithm, it is no longer necessary to carry out a one-dimensional Fourier transform but to carry out matching filtering for the azimuth and range of the spectrum plane of 4f system at the same time. In this way, it is not necessary to introduce a cylindrical lens, only a spherical lens is needed to realize the Fourier transform imaging of SAR. Finally, a two-dimensional SAR processing optical system is built to obtain the SAR image in real time.

The paper provides a review into the steps involved in acquiring and processing synthetic aperture radar (SAR) data transmitted by the European remote sensing (ERS) satellites. The paper reports on a simple MATLAB-based SAR processing system, that reads the image out of the complex SAR data files and that is suitable for use in the classroom to demonstrate one of the procedures used in SAR data processing. The paper can also help a beginner in the field of SAR signal processing to get information and understand the basics that are necessary to acquire and process a SAR image. The data, provided by the Alaskan Satellite Facility (ASF), is categorized into different levels and the paper describes the process of obtaining the level-1 basic image from the level-0 raw data file provided by ASF.

In the area of airborne synthetic aperture radar (SAR), motion compensation (MOCO) is a crucial technique employed to correct the SAR data affected by nonlinear platform trajectory during data acquisition. Due to range-azimuth coupling and computational burden consideration, some approximations, which are valid for SAR systems of moderate aperture length, are usually adopted in commonly used MOCO approaches. However, a much more accurate SAR data processing approach is appealing to process the low-frequency SAR systems with large aperture length, such as P-band. In this paper, a new MOCO approach with high precision and high efficiency is proposed. After the ω - κ processing and the range-dependent MOCO, the analytical expression of a 2-D spectrum of a partially focused SAR image is given. Afterward, aperture reduction is achieved by a chirp modulation technique. Finally, with high precision and less computation cost, back projection along the new built short apertures (affected by the residual motion errors) is employed to yield a fairly well-focused SAR image. Experimental results on simulated and actual P-band SAR data are presented to verify the performance of the proposed approach.

The usual operation of a Synthetic Aperture Radar (SAR) assumes that the sensor platform moves at a constant velocity along a straight line and that objects to be imaged are stationary. The relative motion between the sensor and the objects produces a systematic set of Doppler frequencies which varies as the objects pass through the radar beam. These Doppler histories are recorded and processed to produce the fine resolution radar image of the objects. Propagating ocean waves as well as surface currents are not stationary and consequently perturb the Doppler histories in the SAR data. These perturbations can be troublesome when imaging gravity waves because the wave images are dispersed and thus defocused in the azimuth (along-track) direction. This defocusing can be compensated in the processor by readjusting the azimuth focus by an amount proportional to approximately the phase velocity of the wave. The perturbations of the Doppler histories can also be useful since they contain information pertaining to wave height and surface currents. Radial (line of sight) motion of ocean waves imaged by a SAR also creates problems in processing. Radial motion causes both an abnormal curvature in the recorded signal histories as well as a shift in the average Doppler frequencies. This effect can be partially compensated in the SAR processing by rotation of the cylindrical lenses by an amount proportional to the radial velocity. Perturbations of the SAR Doppler histories can also be used as a diagnostic tool to aid in the study of the SAR imaging mechanism of ocean surfaces. Adaptive manipulation of Doppler histories can be performed relatively easily in the SAR optical processor and the resulting wave imagery examined.

Synthetic aperture Radar (SAR) has been extensively used for space-borne Earth observations in recent times. In conventional SAR systems analog beam-steering techniques are capable of implementing multiple operational modes, such as the Stripmap, ScanSAR, and Spotlight, to fulfill the different requirements in terms of spatial resolution and coverage. Future RADAR satellites need to resolve the complex issues such as wide area coverage and resolution. Digital beam-forming (DBF) is a promising technique to overcome the problems mentioned above. In communication satellites DBF technique is already implemented. This paper discuses the relevance of DBF in space-borne RADAR satellites for enhancements to quality imaging. To implement DBF in SAR, processing of SAR data is an important step. This work focused on processing of Level 1.1 and 1.5 SAR image data. The SAR raw data is computationally intensive to process. To resolve the computation problem, high performance computing (HPC) is necessary. The relevance of HPC for SAR data processing using an off-the-shelf graphical processing unit (GPU) over CPU is discussed in this paper. Quantitative estimates on SAR image processing performance comparisons using both CPU and GPU are also provided as validation for the results.

Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method.

This Study proposes the approach for crop classification using the Grey Level Co-occurrence Matrix feature of Synthetic Aperture Radar (SAR) images. The method utilizes the SAR Images acquired by Sentinel 1A SAR Data and extract textural features using GLCM. In this study, we investigate the potential of Grey Level Co-occurrence Matrix (GLCM)-based texture information for horticulture crop classification with SAR images in Kharif and cloud weather condition. A study on Synthetic Aperture Radar (SAR) satellite imagery was conducted in Chhattisgarh with the objective to evaluate the potential of different texture parameters among crop. The SAR data were pre-processed for textural analysis having entire angle and equal distance quantization. The results were categorized among different parameters showing significant variation for horticulture crops for Contrast, Dissimilarity, Homogeneity, ASM, Energy, Entropy and GLCM Mean. The statistical analysis was done for fruit crop along with major kharif crop of study area. The results shows that mean backscatter value was lowest for banana (99.12 dB) and highest for Mango (198.26 dB) regarding contrast textural property in VH Channel whereas mean backscatter value in VH Channel w.r.t to energy was maximum for banana (0.60 dB) followed by papaya (0.49 dB) and guava (0.45 dB) and least for mango (0.44 dB). The mean backscatter value for GLCM mean textural property in VH channel was shown maximum by banana (51.24 dB) followed by papaya (41.96 dB) and mango (32.98 dB). These results indicate the usefulness of texture information for classification of SAR images, particularly when acquisition of optical images is difficult in Kharif and cloud weather condition for crop classification. Thus GLCM feature of SAR Data proven to be significant for the classification of horticulture crops.

Synthetic aperture radar (SAR) is able to form high-resolution images, which is beneficial for target identification and recognition. However, High-resolution SAR image formation is of immense computation, especially for large scene. In practice, the region of interest (ROI) that needs high resolution imaging may constitute only a small portion of the whole scene. Since ROI detection may only require image resolution much lower than the highest resolution that can be achieved from SAR data, it is efficient to generate an "adequately low resolution" image to find ROI. If target recognition is needed, full-resolution images can be generated for ROI. In this paper, a novel two-stage framework of SAR image formation and processing is developed. Fast factorized back-projection (FFBP) algorithm is introduced to generate SAR images recursively. The FFBP significantly reduces computation complexity with minimal loss in accuracy compared with traditional back-projection (BP) algorithm through recursive stages. At each stage the resolution of images is finer than previously. When the resolution is adequate for target detection, a detection procedure is processed to find ROI in the whole scene. Full resolution image for ROI is then generated by integrating the low resolution images. Moreover, since moving targets appear at different positions in the "adequately low-resolution" image series, moving targets can be also detected. Simulation results show that with single-pass high-resolution SAR data, the method generates low-resolution images quickly for ROI and moving target detection, and provides high-resolution details efficiently for ROI.

Deep learning in remote sensing has received considerable international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in synthetic aperture radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this article, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state of the art of deep learning applied to SAR, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet underexploited field and to pave the way for the use of deep learning in big SAR data processing workflows.

Four-dimensional synthetic aperture radar (SAR) imaging, also known as differential SAR tomography, is a new research topic in the framework of coherent multitemporal/multibaseline SAR processing that extends the interferometry concept. Four-dimensional SAR imaging-based processing could improve the capability of ground-scatterer monitoring with respect to classical differential interferometric SAR processing. The first results on the applicability of such an advanced tomographic SAR processing to real spaceborne data were recently discussed in the literature. In this letter, we present the results of an experiment with a data set that demonstrates the potentialities of this new technique for monitoring complex targets, such as infrastructures.

Synthetic aperture radar (SAR) and optical imaging are different remote sensing methods. Given a SAR image, is it possible to predict what the observed scene looks like in an optical image? Transfer between SAR data and optical data seems to be impossible. However, this article shows examples that by applying deep learning techniques on high resolution airborne SAR images and GoogleEarth optical images, the SAR images and optical images can transfer with each other. The transferring help us to better understand the relationship between SAR and optical image, and can be potentially used to transfer detection or classification algorithms for optical image straightforwardly to be applied on SAR image.

The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) offers a new degree of freedom for radar techniques, although to date its application to synthetic aperture radar (SAR) imaging has not yet been widely reported. In this letter, a SAR imaging model based on vortex EM waves is derived. Ku-band vehicle-borne vortex EM waves SAR experiment using phased uniform circular array antennas is reported for the first time. A detailed comparison with conventional plane EM waves SAR imaging is given in terms of signal-to-noise ratio and azimuth resolution. The experimental results demonstrate the effectiveness of the proposed imaging model, and indicate that vortex EM waves can be exploited in SAR imaging with higher azimuth resolution than plane EM waves. OAM, which is completely independent of frequency and polarization, is a new degree of freedom in SAR imaging. This work shows the new applications of vortex EM waves, which paves the way for the development of novel SAR systems.

The main objective of Synthetic Aperture Radar (SAR) processing is converting the raw acquired data into pixels in the final processed image. The signal is spread in the range and azimuth directions, and the signal migrates to other range cells. Range Doppler Algorithm (RDA) is the most widely applied algorithm in SAR data processing. It performs range compression followed by azimuth compression to form the final reconstructed image. Unfortunately, SAR images suffer from many imperfections that will be studied and analyzed in this paper. Firstly, range compression introduces sidelobes that reduce the Signal-to-Noise Ratio (SNR). For this case, a comparison between three configurations of frequency modulated signals is presented. Moreover, the channel effect that causes degradations in the received signals is investigated and regularized de-convolution is used to reduce this effect. Finally, the random noise that causes a pixel-to-pixel variation and appears as a granular noise in the image will be reduced by applying a median filter to the reconstructed image.

The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization of air. The ionized gas forms the plasma sheath wrapped around the hypersonic platform. The plasma sheath will severely affect the propagation of SAR signal and further affect the SAR imaging. Therefore, hypersonic platform-borne SAR imaging should be studied from a physical perspective. In this paper, hypersonic platform-borne SAR imaging under plasma sheath is analyzed. The SAR signal propagation in plasma sheath is computed using scatter matrix method. The proposed SAR signal model is verified by using a ground experiment system. Moreover, the effect of attenuation caused by plasma sheath on SAR imaging is studied under different SAR parameters and plasma sheath. The result shows that attenuation caused by plasma sheath will degrade the SAR imaging quality and even cause the point and area targets to be submerged into the noise. The real SAR images under plasma sheath also illustrate this phenomenon. Furthermore, by studying imaging results under different SAR and plasma parameters, it can be concluded that the severe degradation of SAR imaging quality appears at condition of high plasma sheath electron density and low SAR carrier frequency. The work in this paper will be beneficial for the study of hypersonic platform-borne SAR imaging and design of hypersonic SAR imaging systems in the future.

The wavenumber domain algorithm is an ideal solution for high-resolution and highly squinted (HRHS) synthetic aperture radar (SAR) imaging in the case of an ideal straight trajectory. However, for airborne HRHS SAR imaging, the Stolt mapping leads to nonsystematic range cell migration (NsRCM) and secondary range compression (SRC) for the HRHS SAR, which causes the HRHS SAR image to defocus severely. To obtain a well-focused HRHS SAR image, a new autofocusing algorithm for HRHS SAR imagery based on an equivalent broadside model is proposed in this article. After coarse motion compensation, linear range walk correction and azimuth resampling are employed to transform the HRHS SAR data into the equivalent broadside SAR data, which has been proved to greatly reduce NsRCM and SRC. After that, the motion error prior structure in the 2-D wavenumber domain is revealed. According to the structure, a novel 2-D wavenumber domain autofocusing algorithm is proposed by the relationship between the 1-D azimuth phase error and the 2-D wavenumber domain phase error correction. Finally, the well-focused HRHS SAR imagery is obtained. Experiments based on simulated and acquired data are carried out to verify the necessity and effectiveness of the proposed algorithm for HRHS SAR imaging.