Accurate Range Modeling for High-Resolution Spaceborne Synthetic Aperture Radar.

Precise range history model and accurate focusing algorithms are challenges for high-resolution spaceborne Synthetic Aperture Radar (SAR) due to the curved orbits of satellites and spatial variations of signal models. The existing range models include HRM, AHRM and DRM. This paper focuses on the range history model for spaceborne SAR. In the paper, an accurate range model based on fourth-order Doppler parameters is proposed to accurately formulate the range history between SAR and targets. Compared with other models, such as DRM4 which is also based on fourth-order Doppler parameters, the proposed range model has much improved accuracy. All the simulation results validate the high precision of the proposed range model.

When processing high-resolution spaceborne synthetic aperture radar (SAR) data, the orbit curvature is a key aspect that must be taken into account. The non-hyperbolic range history makes most SAR imaging approaches not suitable for the curved orbit. Based on the two-dimensional spectrum derived by series reversion (SR), a modified Omega-K algorithm (OKA) is proposed in this paper. Making use of the reference function calculated by SR, an accurate bulk compression is implemented. Following, a modified Stolt interpolation is applied based on least square (LS), to perform the residual range-variant processing efficiently. The method described can achieve satisfactory focusing results for spaceborne SAR, without a large number of computation. Point targets simulations have validated the presented research.

Here we analyse high-resolution spaceborne synthetic aperture radar (SAR) measurements to retrieve statistical and dynamical properties of ocean eddies in the ice-free regions and the marginal ice zone (MIZ) of Fram Strait. Analysis of historical Envisat ASAR images allowes to obtain detailed spatial statistics of eddy generation sites, their diameters and vorticity sign. As shown, the total number of cyclones strongly dominates over anticyclones. Eddies are frequently observed over continental slope and shelf regions around Svalbard, however, about 80% of them are identified over the deep Fram Strait regions. In general, eddies range in size between 1.5 km and 64 km with a mean value of 8 km. Larger eddies of 10-30 km in diameter are usually found over deep water, while smaller eddies of 3-7 km in diameter prevail around Svalbard. As observed, the overall pattern of eddy field in the strait attributes well to the position of West Spitsbergen Current jet and its southern recirculation branch known from literature. Analysis of sequential Sentinel-1 images allows to retrieve surface current velocity field having a clear signature of an anticyclonic eddy in the Fram Strait marginal ice zone. Mean orbital velocity of the observed eddy is estimated to be around 0.4-0.5 m/s, in good agreement with earlier field observations. Presented results demonstrate the potential of spaceborne SAR data to retrieve critical properties of eddies of various scales, and further investigate their role in the Arctic Ocean.

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.

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.

ABSTRACTRecently, the performance of high-resolution spaceborne synthetic aperture radar (SAR) data for urban landscape monitoring has attracted increased interest, in particular for regions where rainy weather prevails throughout of year. In this study, the phase component of SAR data was exploited for landscape monitoring in the western area of Nanjing city, China. Twenty-six scenes of TerraSAR-X single-look complex (SLC) interferometric images were acquired for the period of May 2013 to February 2015 and applied for urban change detection as well as surface subsidence monitoring by means of interferometric SAR (InSAR) technologies. Firstly, a small baseline strategy in conjunction with a fast statistically homogeneous pixel selection (FaSHPS) approach were applied for the coherence imagery generation and for the coherent change detection, resulting in a correct probability of better than 90%. Second, the surface deformation was derived with millimetre accuracy using an enhanced multi-temporal InSAR (MT-InSAR) tool by synergistically analyzing the amplitudes and phases of the SAR acquisitions. This study indicates that high-resolution SAR data have the potential for urban landscape monitoring by taking advantage of the interferometric performance and the all-day and all-weather imaging capability.

The space-borne Synthetic Aperture Radar (SAR) Interferometric (InSAR) technique provides a powerful tool for surveying the surface changes covering areas from several to a hundred kilometers in size at a theoretical accuracy in the millimeter-range. It is widely applied for detecting deformations caused by geophysical and tectonic activities, such as earthquakes, volcanic activities, etc. Nevertheless the accuracy and availability of deformation measurements using InSAR techniques is limited due to decorrelation effects, atmospheric disturbances, the SAR side-looking geometry (which causes layover and shadowing). In order to overcome the limitations, advanced techniques have been developed based on high-resolution SAR acquisitions. In this paper, we will present our recent researches and achievements on advanced SAR and InSAR techniques using both high-resolution and wide-swath SAR images generated from recent satellite missions: TerraSAR-X and TanDEM-X.

Abstract. We show that high-resolution space-borne Synthetic Aperture Radar (SAR) imagery with pixel sizes well below 1 m2 can be used to complement archaeological surveys in areas that are difficult to access. After major storm surges in the 14th and 17th centuries, vast areas on the German North Sea coast were lost to the sea. Areas of former settlements and historical land use were buried under sediments for centuries, but when the surface layer is driven away under the permanent action of wind, currents, and waves, they appear again on the Wadden Sea surface. However, the frequent flooding and erosion of the intertidal flats make any archaeological monitoring a difficult task, so that remote sensing techniques appear to be an efficient and cost-effective instrument for any archaeological surveillance of that area. Space-borne SAR images clearly show remnants of farmhouse foundations and of former systems of ditches, dating back to the 14th and to the 16th/17th centuries. In particular, the very high-resolution acquisition (staring spotlight) mode of the German TerraSAR/ TanDEM-X satellites allows for the detection of various kinds of residuals of historical land use with high precision. In addition, we also investigate the capability of SARs working at lower microwave frequencies (on Radarsat-2) to complement our archaeological survey of historical cultural traces, some of which have been unknown so far.

Spaceborne synthetic aperture radar (SAR) is a promising remote sensing technique, as it can produce high-resolution imagery over a wide area of surveillance with all-weather and all-day capabilities. However, the spaceborne SAR sensor may suffer from severe radio frequency interference (RFI) from some similar frequency band signals, resulting in image quality degradation, blind spot, and target loss. To remove these RFI features presented on spaceborne SAR images, we propose a multi-dimensional calibration and suppression network (MCSNet) to exploit the features learning of spaceborne SAR images and RFI. In the scheme, a joint model consisting of the spaceborne SAR image and RFI is established based on the relationship between SAR echo and the scattering matrix. Then, to suppress the RFI presented in images, the main structure of MCSNet is constructed by a multi-dimensional and multi-channel strategy, wherein the feature calibration module (FCM) is designed for global depth feature extraction. In addition, MCSNet performs planned mapping on the feature maps repeatedly under the supervision of the SAR interference image, compensating for the discrepancies caused during the RFI suppression. Finally, a detailed restoration module based on the residual network is conceived to maintain the scattering characteristics of the underlying scene in interfered SAR images. The simulation data and Sentinel-1 data experiments, including different landscapes and different forms of RFI, validate the effectiveness of the proposed method. Both the results demonstrate that MCSNet outperforms the state-of-the-art methods and can greatly suppress the RFI in spaceborne SAR.

Spaceborne synthetic aperture radar (SAR) and ground-based radar interferometers (GBRIs) can be used to detect spatially detailed crustal deformations that are difficult to detect by on-site observations, the Global Navigation Satellite System, tiltmeters, and so on. To make such crustal deformation information readily available to those engaged in evaluating volcanic activities and researching the mechanisms, we are preparing a database within the Japan Volcanological Data Network data sharing system to store crustal deformation detected by spaceborne SAR and GBRIs (Subtheme 2-1, Project B, the Integrated Program for Next Generation Volcano Research and Human Resource Development). In this study, we examined methods to reduce atmospheric delay noise in SAR interferometry using the numerical weather model and determined the methods for resampling the analytical values of the numerical weather model and estimating atmospheric delay to efficiently determine atmospheric delay. We show that the atmospheric delay can be estimated with higher accuracy by properly combining the isobaric surface and ground surface data of the mesoscale model (MSM) provided by the Japan Meteorological Agency. We are developing a multi-type portable SAR system as a GBRI system such that it would allow campaign observations whenever increased volcanic activities are observed and acquire crustal deformation with a higher temporal resolution than spaceborne SAR for storage in the database. This system employs L-band radar, which has a higher penetrability against vegetation. Two modes of observations are possible: ground-based SAR and car-borne SAR. The prototype was fabricated to conduct experiments necessary to develop a working model. The experimental observations was carried out around Asama volcano, and we confirmed that clear fringe was obtained.

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.

To achieve high-resolution and wide-swath (HRWS) imaging, a space-time waveform-encoding (STWE) spaceborne synthetic aperture radar (SAR) system is adopted. In rugged terrain, the beam-pointing mismatch problem will appear when the traditional digital beamforming (DBF) technique is used to separate the received echoes. This problem leads to decreasing the received echo’s gain, deteriorating the quality of the image product and making the interpretation of SAR image difficult. To address this problem, an advanced echo separation scheme for STWE spaceborne SAR based on the DBF and blind source separation (BSS) is put forward in this paper. In the scheme, the echoes are transmitted within the adjacent pulse repetition intervals to simulate multiple beams, and the scattered echoes are received by the sixteen-channel antennas in elevation simultaneously. In post-processing, a detailed flow is adopted. In the method, the DBF is firstly performed on received echoes. Due to the error caused by terrain undulation, the degree of echo separation is not enough. Then, the BSS is performed on the multiple echoes obtained after the DBF processing. Finally, the highly separated echo signal can be obtained. In this scheme, there is no need to perform the direction of arrival (DOA) estimation before the DBF processing, which saves valuable computing resources. In addition, to verify the proposed scheme, point target and distributed target simulations based on the 16-channel data of an elevation X-band DBF-SAR system are carried out. The results of point targets indicate that the residual echo caused by rough terrain can be reduced by more than 14 dB using the proposed scheme. The proposed scheme can be directly implemented into existing SAR systems; thus, it does not increase the complexity of the system design. The scheme has the potential to be applied to future spaceborne SAR missions.

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel—2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method.

This paper first summarizes the state of the art in spaceborne SAR systems and applications. The second part of this paper gives an overview of new concepts, techniques and technologies for future SAR systems, allowing an increase of flexibility the SAR operation mode as well as a reduction in the overall system costs. Several innovative concepts and technologies as bi and multistatic configurations, parasitic SAR, sparse aperture systems and digital beamforming will play an important role for future spaceborne SAR constellations.

This paper discusses smart antennas for space-borne synthetic aperture radar (SAR). First, some recent development in smart antennas for space-borne SAR is reviewed. Then, the paper presents a low-cost space-borne SAR system using digital beam forming on receive. The smart antenna system is also discussed, and some results are shown. The antenna system, consisting of a parabolic reflector and multi-feed array, is designed and optimized for dual-band dual-polarized digital beam-forming performance. The operating frequencies are at X and Ka bands with the center frequency of 9.6 and 35.75 GHz, respectively. The stacked dipoles and square patches with parasitic elements are employed as the feed elements at X and Ka bands. Dual-band antenna arrays are combined in the same aperture, which not only reduce the aperture of the feed array, but also coincide the center of dual-band feed arrays.