MCSNet: A Radio Frequency Interference Suppression Network for Spaceborne SAR Images via Multi-Dimensional Feature Transform

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.

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.

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.

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.

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.

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.

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) 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.

The spaceborne Synthetic Aperture Radar (SAR) is a broadly acknowledged technique to monitor the vast open ocean. However, a frequent confusion between the SAR image azimuth angle <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> and the satellite flight direction <inline-formula>$ {\phi }_{\mathrm{S}\mathrm{A}\mathrm{T}} $</inline-formula> exists in the data processing and geophysical applications, which influences for instance the accuracy of SAR wind inversions. An overview of <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> derivation from the transformation of the spatial satellite coordinate systems is presented in this paper, along with a method for calculating <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> based on the Ground Control Points (GCP). By analyzing a set of 0.7 million Sentinel-1 SAR wave mode images acquired in 2016, we quantify the systematic bias between <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> and <inline-formula>$ {\phi }_{\mathrm{S}\mathrm{A}\mathrm{T}} $</inline-formula> and find out that this bias is related to the incidence angle and orbital direction. It can be accurately fitted using a fifth-order polynomial relative to latitude. The misuse of <inline-formula>$ {\phi }_{\mathrm{S}\mathrm{A}\mathrm{T}} $</inline-formula> is demonstrated to approximate <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> can lead to nonnegligible wind retrieval errors. The spatial distribution of this wind error is non-uniform and differs between ascending and descending orbits, with a wind speed bias of ±0.5 m·s^–1. Results obtained in this study evidence that the precise estimate of <inline-formula>$ {\phi }_{\mathrm{I}\mathrm{M}\mathrm{G}} $</inline-formula> are significant to various SAR applications in relevant studies. The proposed method here also holds practical reference for other satellite SAR missions.

Radio frequency interference (RFI) is a critical issue that leads to image degradation of spaceborne synthetic aperture radar (SAR) systems. In addition to the interference of ground terrestrial radiation sources, there is an emerging type of interference called mutual terrain scattered interference (MTSI), which is becoming more and more severe with the deployment of multiple satellite constellations. In this paper, we investigated the mechanism of MTSI, analyzed the signal characteristics, and discussed the boundary conditions from the time, frequency and space perspectives. Simulation results are provided for illustration, which provides a knowledge basis for interference detection and mitigation.

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.

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.

The classification and detection of maritime targets are widely used in shipping navigation and military fields. With the development of spaceborne synthetic aperture radar (SAR) technology, more and more very high-resolution SAR images can be acquired for maritime targets recognition. However, due to the different imaging mechanisms between SAR images and optical images, it is difficult and laborious to interpret SAR images manually. This study uses a modified Alexnet structure to realise maritime targets classification on the Gaofen-3 spaceborne SAR images. The maritime targets dataset (MTD), including boats, cargo ships, container ships, windmills, oil tankers, and iron towers, is conducted. Moreover, the proposed convolution neural networks (CNNs) structure is trained and tested on the MTD. Experimental results show that the model can get an accuracy of 92.10% in classifying the six kinds of targets, and the performance is superior compared with other CNNs and traditional supportive vector machine algorithms.

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.

In studying image formation methods of the spaceborne synthetic aperture radar (SAR), we utilize its modeling and simulation (M&amp;S) to generate its realistic simulated rawdata. Especially, for the spaceborne spotlight SAR, we perform M&amp;S that reflects its real characteristics, and get rawdata that are almost identical to one acquired by the real SAR sensor. Particularly, operations of the spaceborne spotlight SAR are simulated based on models of its dynamics and geometry related to timeline, orbital state vector, antenna beam pattern, azimuth beam steering, and etc. In addition, the target observation of it is modeled as evaluating observation angles related to point targets within the acquisition time. Finally, based on the received echo signal model, rawdata are simulated for point targets taking into account its real operation. For the high resolution SAR image formation, simulated rawdata are focused with the extended chirp scaling algorithm. Especially, its range cell migration (RCM) factor is the key one for the exact range cell migration correction. In order to do it accurately, the Doppler frequency and the effective velocity have to be calculated correctly for all range sample bins. For precise processing, we suggest the method to analyze them using orbital state vectors and scene coordinates based on two way slant range model. In experiments, system parameters and imaging scenarios to simulate rawdata acquisition of the spaceborne spotlight SAR system are defined. The processing results for realistic simulated rawdata of it are presented to evaluate the performance and the effectiveness of proposed methods. Its results show that suggested methods are applicable to form the high resolution spaceborne spotlight SAR image.