Ultra-High-Resolution Spaceborne and Squint SAR Imaging for Height-Variant Geometry Using Polynomial Range Model

The scientific developments carried out in the last years with spaceborne synthetic aperture radar (SAR), as well as the comparisons with in-situ sensors, have demonstrated that SAR is a reliable remote sensing tool to study wave fields on the open ocean. In their so-called SAR image mode, SAR systems on board satellites are able to scan ocean areas about 100 × 100 km2 with a spatial resolution about 20 × 20 m2. These SAR images are able to provide information about the spatial variability of wave fields, as well as other phenomena that occur on the ocean surface, such as the local wind field. This work investigates the capabilities of spaceborne SAR to extract sea state information for those areas close to coastal locations, where the incoming wave fields present high spatial inhomogeneities. For this purpose, ERS-2 SAR and ENVISAT Advanced SAR (ASAR) images acquired over the North Sea and the Bay of Biscay are used to study ocean waves in shallow waters under different climate and oceanographic conditions. In addition, the potential of ENVISAT ASAR capability to scan the ocean surface with dual polarizations (horizontal, HH, and vertical, VV) is analyzed in this work. The relevance of these investigations for different practical applications concerning the analysis of ocean waves is discussed.

This paper proposes the precise spaceborne synthetic aperture radar (SAR) image formation technique based on the analysis of critical error factors that severely degrade the SAR image quality. These are error factors related to the antenna beam pointing, the effective velocity, and the Doppler centroid. We newly developed the spaceborne SAR system simulator which is able to analysis effects that critical errors of the user-designed spaceborne SAR induce on a focused image. Using it, effects of critical errors are analyzed for spaceborne SAR image formation. Analysis results show that these cause phase distortion of rawdata, distort the symmetry of the azimuth impulse response function (IRF) of point targets in the focused image, and defocus the SAR image. To resolve these problems, we suggest to make use of the phase gradient algorithm (PGA) to compensate phase distortion induced by antenna beam pointing errors. Also, the effective velocity of the illuminated beam and the Doppler centroid of spaceborne SAR rawdata are exactly calculated by proposed methods using both orbit state vectors and the rawdata acquisition geometry based on the newly defined twoway slant range equation model. Furthermore, azimuth block processing is used to reduce signal level of ambiguities produced by sidelobes of antenna beam. The experimental results on the simulated SAR data show that proposed methods are able to reduce error effects as well as improve the focused SAR image quality greatly.

High-resolution space-borne synthetic aperture radar (SAR) images are important sources of information for marine monitoring and target recognition. For inshore port scenes, there is a wide variety of ship targets; moreover, as the scattering intensity of the land background is much higher than that of the sea surface, similar structures on land in SAR images can cause significant interference to the identification of ship targets, resulting in a high false alarm rate and false recognition rate. Therefore, the classification of ship targets in SAR images still requires professional interpretation and correction to eliminate possible false alarms and recognitions. In this paper, by imitating the basic structural requirements for manual discrimination of target categories, a space-borne SAR high-resolution inshore ship classification and correction method of “Pre-recognition – Fine Recognition – Matching Correction” is designed based on the YOLO recognition network. Taking three types of typical ship targets as examples, a high-resolution SAR inshore ship target dataset and a high-resolution SAR ship component architecture dataset are constructed to train the pre-recognition network and the fine recognition network respectively. The latter is based on the former to recognize the component composition of possible targets, and forms a component arrangement architecture vector. The final recognition results are obtained by matching and correcting the similarity distance with the three types of targets. This method can reduce the false alarm rate in ship target recognition in complex inshore port scenes to less than 10% and correct some false recognition results, thereby improving the accuracy of ship target identification and reducing the cost for manual correction.

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. The reason is that the study of SAR imaging processing, SAR image processing and SAR applications are conducted respectively, the integrated study are lacked for parameter selection of SAR imaging processing and SAR image processing and targets identification of SAR application. Focusing on above science problems, the study for phase high fidelity model of high precision interferometric SAR, SAR non-stationary backscattering characteristics and cognition, SAR three-dimensional electromagnetic scattering modeling and its scattering mechanism and SAR multi-parameters optimization information extraction has been conducted. The typical natural distributed targets and man-made targets such as surface deformation, sea ice, building complex and collapsed building are selected, the method and application study oriented to SAR environmental parameters inversion are conduced, the main result and conclusion are as follows: (1) The overall theoretical frames of SAR information integrated processing was proposed; The simulation and ground validation system used to assess the effects of SAR information integrated processing was created; furthermore, the spaceborne SAR imaging algorithm with three steps focusing processing was proposed and realized the multi-modal integrated imaging processing and improved the imaging processing precision. (2) In terms of SAR information integrated processing of natural targets, focusing on phase high fidelity imaging processing of interferometric SAR surface deformation monitoring, an novel spaceborne SAR high fidelity simulation method based on stationary RCS and improved Goldstein SAR interferogram filter based on empirical mode decomposition were proposed. Focusing on multi-parameters optimization processing of sea ice types classification, the multi-channel spaceborne SAR sparse imaging method based on compressive sensing, Kalman filter for removal of scalloping and inter-scan banding in ScanSAR images and SVM sea ice classification method combined with sea ice concentration were proposed. (3) In terms of SAR information integrated processing of man-made targets, focusing on non-stationary backscattering from building complex, mitigation of azimuth ambiguities in spaceborne stripmap SAR iamges using selective restoration, SAR image despeckling by selective 3D filtering of multiple compressive reconstructed images and man-made target detection in urban areas based on a new azimuth stationary extraction method were proposed. Focusing on three-dimensional modeling and fast extraction of collapsed building, H-a-p method used for collapsed building extraction was proposed.

A new region growing based algorithm for extracting roads in spaceborne synthetic aperture radar (SAR) images is presented in this paper. The basic idea is that an initial seed point moves in a determined direction with a certain step length. The algorithm is composed of five sequential steps: the first is to search initial seed point and the second is to choose a circular window size similar to the width of the first road. In the third step, the criteria for region growing are chosen, and the fourth is to determine the key elements of new roads growing. At last, stopping rules are specified. The experimental results show that the proposed method can better extracted roads in SAR images.

Spaceborne Synthetic Aperture Radar (SAR) is a reliable tool to analyze sea states in the open ocean on a global scale. The sea state information obtained from SAR systems is mainly based on two different approaches. The first approach considers the spectral description of a sea state in terms of the so-called directional wave spectrum. The second more recent approach provides estimations of the wave elevation fields in two dimensions, which permits to detect individual wave heights. Both approaches consider a linear wave field (i.e. a linear superposition of single monochromatic wave components). Although the linear wave theory is sufficient to explain most of the mean statistical features of sea states, it is no able to describe properly some wave phenomena, such as the asymmetries between wave crests and troughs, which are of special interest for off-shore platform and ship designers. This work analyses the effects of nonlinear wave field features in the SAR imaging mechanisms in order to detect these phenomena on the SAR images of the open ocean.

Sidelobe reduction is a very primary task for synthetic aperture radar (SAR) images. Various methods have been proposed for broadside SAR, which can suppress the sidelobes effectively while maintaining high image resolution at the same time. Alternatively, squint SAR, especially highly squint SAR, has emerged as an important tool that provides more mobility and flexibility and has become a focus of recent research studies. One of the research challenges for squint SAR is how to resolve the severe range-azimuth coupling of echo signals. Unlike broadside SAR images, the range and azimuth sidelobes of the squint SAR images no longer locate on the principal axes with high probability. Thus the spatially variant apodization (SVA) filters could hardly get all the sidelobe information, and hence the sidelobe reduction process is not optimal. In this paper, we present an improved algorithm called double spatially variant apodization (D-SVA) for better sidelobe suppression. Satisfactory sidelobe reduction results are achieved with the proposed algorithm by comparing the squint SAR images to the broadside SAR images. Simulation results also demonstrate the reliability and efficiency of the proposed method.

Spaceborne Synthetic Aperture Radar (SAR) is well adapted to detect ocean pollution independently from daily or weather condition. In fact, oil slicks have specific impact on ocean wave spectra. Initial wave spectra may be characterized by three kinds of waves, big, medium and small, which correspond physically to gravity and gravity-capillary waves. The increase of viscosity is due to the presence of oil damps gravity-capillary waves. This induces a damping of the backscattering to the sensor, but also a damping of the energy of the wave spectra. Thus, local segmentation of wave spectra may help oil slick detection. It can be achieved by the segmentation of a multiscale decomposition of the original SAR image. In this work, a supervised oil slick detection is proposed by using Support Vector Machines into the wavelet decomposition of a SAR image. It performs accurate detection with no consideration to signal stationarity nor to the presence of strong backscatters (such as ship). Moreover, when using normalized SAR images, the kernel expansion may be generalized from one image to an other to make a near unsupervised detection scheme. The algorithm has been applied on Envisat ASAR images. First experiments yield accurate segmentation results with a very limited number of false alarms.

A spaceborne synthetic aperture radar (SAR) systematically scans the target scene along its trajectory at various positions and azimuth angles, making it a unique asset for many remote sensing applications. The contrast between ship targets and the surrounding sea can be improved in SAR images by splitting the bandwidth into subapertures (SAs) and then averaging to attain a high azimuth resolution. This letter proposes the use of subapertures decomposition (SD) for detecting moving ships. Furthermore, the ship velocity parameter in the azimuth direction is computed. The proposed SAs dual-pol descriptor ( r SA ) employed the constant false alarm rate (CFAR) approach followed by a statistical Gamma ( Γ ) distribution to determine the threshold value for distinguishing ship and sea. Thereafter, the ships edges are quantified using Sobel and Canny edge detectors. The eigenvalue descriptor ( λ ) of polarimetric SAR provides the total size of the target; hence, the ship sizes are compared with λ . The velocity of moving ships ( V S ) is evaluated by utilizing the mean difference of the Doppler centroid ( Δ f DC ) information between pairs of all SAs and is validated using the SA displacement correlation method. The experimental results demonstrate that the proposed method is feasible for real-time moving ship monitoring with 95% accuracy.

Spaceborne Synthetic Aperture Radar (SAR) instruments are effective tools for monitoring and mapping wetlands. With the availability of SAR instruments providing various polarization options, the scope of this study is to evaluate the new compact polarization for wetland multitemporal change detection using simulated RADARSAT Constellation Mission (RCM) SAR data. A series of fully polarimetric (FP) SAR images were collected over a test site located in Ontario, Canada, and used to simulate RCM compact polarimetric (CP) data. The simulated data were evaluated for multitemporal change detection and the results were compared to those from FP SAR data. WorldView imagery and water level data were used for analysis and validation of the change detection results. The study shows potential for using the CP SAR for multitemporal change detection over three major wetland classes: shallow water, marsh, and swamp. Although FP SAR was slightly more effective in multitemporal change detection compared to CP SAR, the percentage of agreement between the change detection results of FP and CP SAR was always greater than 90% for all wetland classes. The highest overall percentage of agreement (98.4%) between the results of FP and CP SAR was observed over the shallow water, while the lowest (92.5%) was observed over swamp.

Spaceborne synthetic aperture radar (SAR) performance may get degraded due to component degradation, aging, thermal variations of the environment and so on. Performance degradation should be corrected when the spaceborne SAR is in orbit. Calibration is implemented on the radar imagery by radiometric calibration which gives the relationship between digital number and backscattering coefficient. Radiometric calibration gives calibration constant, which is the key parameter to convert the SAR digital numbers into backscattering coefficient. With radiometrically calibrated image, the performance of one sensor can be compared with other sensors through multiple acquisitions to improve quantitative analysis of SAR performance. Radiometric calibration is done by ground deployed corner reflectors (CR). CRs have well-known radar cross sections, and hence, the derived radar cross sections from the SAR image are compared with the theoretical RCS to characterize the SAR sensor. An attempt is made to study the effectiveness of calibration, using different combination of CRs (various sizes and shapes) by deploying CR’s in the Jain Global institutions—JGI global Campus, Jakkasandra, Bangalore, and average sigma-naught (σ0) is estimated through multiple acquisitions of Sentinel-1A. Sentinel-1A was launched by European Space agency in 2014, having SAR as a payload. Sentinel-1A is an open source system which operates in four different modes with different resolution schemes. In this paper, Interferometric Wide swath mode of Sentinel-1A is used as that particular mode of acquisition is available over Jain Global campus. This paper presents the general criteria for selection of CRs and the measures taken during deployment. Integral method is used to estimate the calibration coefficient of Sentinel SAR data with different interpolation factors.

This paper discusses the degradation of space-borne synthetic aperture radar (SAR) imaging due to anisotropic ionospheric irregularities. In the case of the oblique incidence and anisotropy of ionospheric irregularities, two types of potential ionospheric effects on SAR imaging are derived from an improved transverse correlation function. First, in the range direction, the image shift due to both multiple scattering and dispersion of irregularities is considered. In addition, pulse broadening due to multiple scattering of the irregularities is also studied, which leads to degradation of the range resolution. Second, in the azimuthal direction, the decorrelation distance is obtained by the second moment of the generalized ambiguity function. Using the range Doppler algorithm (RDA), several degraded point target responses due to the ionospheric irregularities are evaluated. The simulation results show that the adverse effects of ionospheric irregularities on SAR imaging are worsened at lower frequencies and stronger ionospheric fluctuations.

In spaceborne synthetic aperture radar (SAR), finite sampling of azimuth Doppler signals will lead to azimuth ambiguities. Similar to the main signal, azimuth ambiguities will reduce the accuracy of target detection. Many suppression methods were put forward with good results. But few people paid attention to azimuth ambiguities' auto recognition and usually mark the ambiguities manually. Based on the research of azimuth ambiguities, for ships on the sea, this paper puts forward a new method to detect and mark azimuth ambiguities automatically in SAR images using Faster-RCNN network of deep learning and mutual information (MI). Based on the Gaofen-3 spaceborne SAR images, the maritime boat dataset (MBD) is conducted, including ships of different resolutions. The Faster-RCNN Network is trained and tested using MBD to detect boats on the sea. According to the azimuth ambiguity distance formula and the mutual information between two areas, the ambiguity will be detected. Experiment results shows this method is feasible.

By acquiring information over a wide area regardless of weather conditions and solar illumination, space-borne Synthetic Aperture Radar (SAR) has the potential to be a promising application for traffic monitoring. However, the backscatter character of a vehicle in a SAR image is unstable and varies with image parameters, such as aspect and incidence angle. To investigate vehicle detectability in SAR images for traffic monitoring applications, images of four common types of vehicles in China were acquired using the fully polarimetric (FP) SAR of Radarsat-2 in our experiments. Methods for measuring a vehicle’s aspect angle and backscatter intensity are introduced. The experimental FP SAR images are used to analyze the detectability, which is affected by factors such as vehicle size, vehicle shape, and aspect angle. Moreover, a new metric to improve vehicle detectability in FP SAR images is proposed and compared with the well-known intensity metric. The experimental results show that shape is a crucial factor in affecting the backscatter intensity of vehicles, which also oscillates with varying aspect angle. If the size of a vehicle is smaller than the SAR image resolution, using the intensity metric would result in low detectability. However, it could be improved in an FP SAR image by using the proposed metric. Compared with the intensity metric, the overall detectability is improved from 72% to 90% in our experiments. Therefore, this study indicates that FP SAR images have the ability to detect stationary vehicles on the road and are meaningful for traffic monitoring.

Ship surveillance using multiple remote sensing sensors becomes more and more vital presently. Among the various sensors, space-borne Synthetic Aperture Radar (SAR) is optimal for its high resolution over wide swaths and all-weather working capabilities. Meanwhile, Automatic Identification System (AIS) is efficient to provide ship navigational information. Limited to the progress of ship surveillance using SAR image only, the integration of them significantly benefits more. Data association is the fundamental issue. Many algorithms have been developed including the Nearest-Neighbour (NN) algorithm, the Joint Probabilistic Data Association (JPDA) method, and the Multiple Hypothesis Testing (MHT) approach. Ship positions derived from SAR image can be associated with the ones provided by AIS. State-of-the-art method (NN algorithm) is proved to be feasible. But it faces more challenges under adverse circumstances, such as high-density-shipping condition. We investigate the non-rigid Point Pattern Matching (PPM) method to solve this problem. To the best of our knowledge, this paper is the first to introduce non-rigid PPM to the data association of SAR and AIS. On the basis of introduction to the data association, Coherent Point Drift (CPD) algorithm is investigated. Experiments are carried out and the results illustrate that the CPD algorithm achieves higher accuracy and outperforms state-of-the-art method, especially under high-density-shipping condition.