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SAR image of a location at Kirtland Air Force Base, Albuquerque, N.M., exhibiting 4-inch (10 centimeter) resolution. Note that the aircraft are better defined by their shadows than by their direct echo return.
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The intertwined history of optics and synthetic aperture radar (SAR) is discussed. Most airborne and orbital SAR systems are monostatic, in that they employ a single antenna for transmission and reception of the radar signal. In optics, an imaging lens applies a phase function to a scattered field so that coherent summation occurs at the correct lo...
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... SAR image such as that illustrated in Fig. 1 is usually a two-dimensional (2D) map of the radar reflectivity of a target scene which includes dimensions of range and azimuth. Most airborne and orbital SAR systems are monostatic, in that they employ a single antenna for transmission and reception of the radar signal. The transmitted signal is typically a sequence of modulated ...
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Remote-sensing methods are the primary ones used for ice mapping in the Baltic Sea. A major methodological improvement is now being introduced by satellite radars due to their weather independency and high resolution. To learn how to use ERS-1 synthetic aperture radar (SAR) data, an extensive field programme BEPERS (Bothnian Experiment in Preparati...
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The scale‐invariant feature transform (SIFT) algorithm is the most widely used feature extraction as well as a feature matching method in remote sensing image registration. However, the performance of this algorithm is affected by the influence of speckle noise in synthetic aperture radar (SAR) images. It reduces the number of correct matches as we...
We develop a linearized imaging theory that combines the spatial, temporal, and spectral aspects of scattered waves. We consider the case of fixed sensors and a general distribution of objects, each undergoing linear motion; thus the theory deals with imaging distributions in phase space. We derive a model for the data that is appropriate for any w...
Citations
... Synthetic aperture radar (SAR) deception jamming technology is effective in concealing important military facilities and operational equipment [1,2], enabling covert military operations [3,4]. The SAR deception jamming technology has the advantage of low power requirement, making it a popular research topic in SAR jamming technology [5][6][7][8]. ...
... By augmenting the existing templates, an efficient library of SAR deception jamming templates with shadows can be established. 2 Currently, there are two types of sample augmentation schemes for SAR deception jamming templates with shadows, traditional schemes and deep learning-based schemes. The first type involves traditional techniques, such as translation, rotation, and scaling, to obtain augmented SAR deception jamming template libraries with shadows [12]. ...
To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of templates is low due to the lack of speckle noise. Second, the generated templates have low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposes a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solves the two aforementioned problems from the following aspects. First, the influence of the speckle noise is considered in the network to avoid the problem of reduced authenticity in generated images. Second, a channel attention mechanism module is used to improve the network's learning ability of shadow features, which improves the similarity between the generated template and the shadow area in the input template. Finally, the proposed scheme and the SinGAN scheme are compared regarding the equivalent numbers of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrate that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme have targets and shadow features similar to those of the original image, and can incorporate speckle noise characteristics, resulting in higher authenticity, which helps to achieve fast and effective SAR deception jamming.
... Structural remote sensing of forests was initially performed via electro-optical sensing or synthetic aperture radar (SAR). SAR systems emit low-frequency pulses capable of measuring tree height or penetrating forest canopies to detect the ground and static targets [6]. However, the precision and recall of results from older or heterogeneous forests was deemed to be inadequate. ...
Light detection and ranging (lidar) forest models are important for studying forest composition in great detail, and for tracking objects in the understory. In this study we used DIRSIG, a first-principles and physics-based simulation tool, to turn the lidar data into voxels, towards classifying forest voxel types. A voxel is a 3D cube where the dimension represents a certain distance. These voxels are split into categories consisting of background, leaf, bark, ground, and object elements. Voxel content is then predicted from the provided simulated and real National Ecological Observation Network (NEON) data. The inputs are 3D neighborhood cubes which surround each voxel, which contain surrounding lidar signal and content type information. Provided simulated data are from two sources: a VLP-16 drone, which collects discrete lidar data close to the canopy, and the NEON Airborne Observation Platform (AOP), which is attached to an airplane flying 1000 m above ground level and collects both discrete and waveform lidar data. Different machine learning algorithms were implemented, with 3D CNN algorithms shown to be the most effective. The Keras library was used, since creating the layers with the sequential model was regarded as an elegant approach. The simulated VLP-16 waveform data were significantly more accurate than the simulated NEON waveform data, which was attributed to its proximity to the canopy. Leaves and branches exhibited acceptable accuracies, due to their relatively random shapes. However, ground and objects in both cases had very high accuracy due to the high intensities and their rigid shapes, respectively. A sample of real NEON waveform lidar data was used, though the sample primarily focused on the canopy region; however, most of the voxels were correctly predicted as leaves. Additional channels were added to the input voxels in order to improve accuracy. One input parameter which proved to be very useful were the local z-values of each input array. Additionally, the Keras Tuner framework was used to obtain improved hyperparameters. The learning rate was reduced by a factor of 10, which provided slower, but steadier convergence towards accurate predictions. The resulting accuracies from the predictions are promising, but there is room for improvement. Different ML algorithms that use the point cloud should also be considered. Further segmentation of forest classes is another possibility. For example, there are different types of trees and bushes, so each tree or bush could have its own unique classes, which would make predicting the shapes much easier. Overall, discovering a method for accurate object prediction has been the most significant finding. For the ground truth models, the best object precision is approximately 99% and the best recall is 78%.
... Synthetic Aperture Radar (SAR) systems are an adaptation of RAR systems, enabling the computation of the DOA through forming a synthetic aperture. Synthetic aperture means that observations of a large antenna (narrow beam, i.e. high angular resolution) can synthetically be created by many observations carried out along a trajectory in space by a smaller antenna (wide beam, i.e. low angular resolution) [49]. In case of terrestrial SAR systems, the radar antennas are mounted on a platform -as for example on a relatively short rail [4,56] ( Fig. 2.2c) or a car [43,115,200] and the data are acquired, while the antennas move along the given trajectory. ...
Due to constant exposure to environmental conditions and external forces, engineering structures like bridges, high-rise buildings, and others deteriorate over time. Structural Health Monitoring (SHM) aims to identify and locate potential damages that could cause a change in the system’s integrity. Identification can help reduce costs by initiating timely maintenance and extending the structure’s lifetime. Engineers use various types of sensors (e.g. accelerometers, strain gauges, etc.) to assess the structure’s condition. Most systems provide a time series of observations at the sensor’s location. Covering large structures would require the costly installation of multiple sensors and wiring a network for acquisition management. MIMO-SAR, short for Multiple Input Multiple Output Synthetic Aperture Radar, systems are an emerging alternative. By emitting a frequency-modulated continuous wave (FMCW), such systems can use Terrestrial Radar Interferometry (TRI) to measure highly accurately the displacements of an object at high temporal and spatial resolution. However, the effects impacting the performance of MIMO-SAR systems are yet not well understood for practical applications.
In this thesis, the applicability of W-band MIMO-SAR for SHM has been investigated. More specifically, the effects impacting the accuracy of a commercial low-cost, automotive MIMO-SAR system have been analysed. Experiments carried out in indoor and outdoor environments under adverse weather conditions have been used to analyse and quantify the impact of measurement noise, short-term drifts due to clock instabilities, meteorological variations, and electromagnetic interference caused by a second active MIMO-SAR system on displacement measurements. This was followed by assessing the capabilities of a MIMO-SAR system for a real-case application, i.e. deformation of a railway bridge under traffic load and deformation of a wind turbine tower under working load. The investigation was rounded off by developing an algorithm to derive 3D displacement vectors from a set of line-of-sight displacements as it is given by TRI. Those algorithms performed least-square adjustments which took into account the spatial or temporal correlations of the observations. The results show that W-Band MIMO-SAR sensors can be used to measure short-term line-of-sight displacement with low uncertainties (tens of micrometres) and high temporal resolution (milliseconds). The system configuration used in these investigations allowed 2D mapping of the displacements of objects located up to 175 metres with high angular (approx. 1.4 degrees) and range (up to 4 centimetres) resolution. Furthermore, measurements acquired by three simultaneously operating MIMO-SAR sensors could be combined to derive 3D displacement vectors coinciding with the actual movement of a point scatterer (corner cube).
The investigations expanded the knowledge regarding the performance and quality of the phase measurements of MIMO-SAR systems operating in the W-band. Their applicability for SHM has been demonstrated on two engineering structures. The results indicate that the MIMO-SAR technology could supplement or even replace classical geodetic and other measurement systems used for deformation monitoring.
... YNTHETIC Aperture Radar (SAR) is a microwave-imaging radar system that can achieve high-resolution imaging [1]. As an active means of microwave remote sensing, SAR offers all-time and all-weather reconnaissance and strong surface penetration, which are widely used in environmental protection, military reconnaissance, surface mapping, and other fields [2]. ...
With the continuous development of synthetic aperture radar (SAR) technology, SAR image data are becoming increasingly abundant. For the same scene, dual-frequency (high-frequency and low-frequency) SAR images can present different details and feature information. SAR image fusion of the two frequencies can combine the advantages of both, thus describing targets more comprehensively. Because high-resolution SAR images contain a large amount of detailed information such as edges and textures, the traditional fusion methods cannot fuse this information better, resulting in a loss of information. To solve the problem, this paper proposes a fusion method suitable for airborne dual-frequency, high-resolution SAR images. Firstly, the source SAR images are decomposed to obtain their low-pass bands and high-pass bands by using the non-subsampled Shearlet transform (NSST). Then, we apply the improved non-negative matrix factorization (NMF) to merge the low-pass bands and use the new sum of modified Laplacian (NSML) to merge the high-pass bands. After that, the fused low-pass bands and high-pass bands are reconstructed by the inverse NSST, to obtain the final fused image. Finally, by processing the airborne SAR data, the effectiveness of the proposed method is verified.
... The traditional millimeter-wave imaging configurations are restricted to variants of synthetic aperture radar (SAR) [1], [4], [5], [6] or phased arrays systems [7], [8]. In the case of SAR, a co-located transmitting and receiving antenna is mechanically moved along one or two directions to synthesize an aperture. ...
Dynamic metasurface antennas (DMAs) have recently been introduced as a computational imaging (CI) platform that offers significant advantages over conventional imaging systems. Such antennas are able to produce tailored radiation patterns that are later used to encode the scene’s information into a few measurements. However, since the signal is compressed, image reconstruction in the frequency domain using the conventional range migration algorithm (RMA) cannot be directly applied to CI-based apertures synthesized with DMAs. More sophisticated algorithms need to be developed to overcome this limitation, which transfers the complexity of such systems to the software layer. This paper proposes a new multistatic DMA RMA (MD-RMA) technique that achieves decompression of the compressed signals collected from multistatic DMA apertures and processes the decompressed data in the Fourier domain followed by a multistatic-to-monostatic conversion. Simulation results verify that the proposed approach can produce high quality 3D radar images, which alleviates the need to mechanically move the aperture. Moreover, since the image reconstruction is fully conducted in the frequency domain, leveraging the fast Fourier transform (FFT) algorithm, the complexity of the algorithm, and thus the execution time, is significantly reduced in contrast with conventional spatial domain algorithms.
... Synthetic aperture radar (SAR) is a microwave-imaging radar system that can achieve high-resolution imaging [1]. As an active means of microwave remote sensing, SAR offers all-time and all-weather reconnaissance and strong surface penetration, which are widely used in environmental protection, military reconnaissance, surface mapping, disaster assessment, and other fields. ...
With the continuous development of synthetic-aperture-radar (SAR) technology, SAR-image data are becoming increasingly abundant. For the same scene, dual-frequency (high-frequency and low-frequency) SAR images can present different details and feature information. Image fusion of the two frequencies can combine the advantages of both, thus describing targets more comprehensively. Image registration is the key step of image fusion and determines the quality of fusion. Due to the complex geometric distortion and gray variance between dual-frequency SAR images with high resolution, it is difficult to realize accurate registration between the two. In order to solve this problem, this paper proposes a method to achieve accurate registration by combining edge features and gray information. Firstly, this paper applies the edge features of images and a registration algorithm based on fast Fourier transform (FFT) to realize rapid coarse registration. Then, combining a registration algorithm based on the enhanced correlation coefficient (ECC) with the concept of segmentation, the coarse-registration result is registered to achieve accurate registration. Finally, by processing the airborne L-band and Ku-band SAR data, the correctness, effectiveness, and practicability of the proposed method are verified, with a root mean square error (RMSE) of less than 2.
... Synthetic Aperture Radar (SAR) is an imaging technique that renders a 2-D image of a 3-D scene of interest. 1 Pixel values are associated with radar reflectivity. Like many imaging systems, a SAR image is typically "focused" to some surface, usually the ground, which is typically assumed to be a horizontal flat planar surface. ...
... Synthetic aperture radar (SAR) [1][2][3] is an important modern microwave sensor system, with powerful capabilities, including high-resolution imaging, day-and-night use, and all-weather operation. Those qualities make it superior to other sensors, such as infrared and optical sensors, for some applications. ...
Synthetic aperture radar (SAR) is an advanced microwave imaging system of great importance. The recognition of real-world targets from SAR images, i.e., automatic target recognition (ATR), is an attractive but challenging issue. The majority of existing SAR ATR methods are designed for single-view SAR images. However, multiview SAR images contain more abundant classification information than single-view SAR images, which benefits automatic target classification and recognition. This paper proposes an end-to-end deep feature extraction and fusion network (FEF-Net) that can effectively exploit recognition information from multiview SAR images and can boost the target recognition performance. The proposed FEF-Net is based on a multiple-input network structure with some distinct and useful learning modules, such as deformable convolution and squeeze-and-excitation (SE). Multiview recognition information can be effectively extracted and fused with these modules. Therefore, excellent multiview SAR target recognition performance can be achieved by the proposed FEF-Net. The superiority of the proposed FEF-Net was validated based on experiments with the moving and stationary target acquisition and recognition (MSTAR) dataset.
... Synthetic aperture radar (SAR) has been an important and powerful modern microwave sensor system in both military and civilian areas [1]. Due to its superior operational capabilities [2,3], SAR has played a significant information acquisition role for reconnaissance and detection nowadays. In addition, SAR can obtain the electromagnetic scattering characteristics of the detected targets and scenarios and acquire unique information from the imaging results at microwave frequencies [4], which have been of remarkable superiorities compared with other sensor systems. ...
Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.
... Synthetic Aperture Radar (SAR) is an imaging technique that renders a 2-D image of a 3-D scene of interest. 1 Pixel values are associated with radar reflectivity. Like many imaging systems, a SAR image is typically "focused" to some surface, usually the ground, which is typically assumed to be a horizontal flat planar surface. ...
Synthetic Aperture Radar (SAR) projects a 3-D scene’s reflectivity into a 2-D image. In doing so, it generally focusses the image to a surface, usually a ground plane. Consequently, scatterers above or below the focal/ground plane typically exhibit some degree of distortion manifesting as a geometric distortion and misfocusing or smearing. Limits to acceptable misfocusing define a Height of Focus (HOF), analogous to Depth of Field in optical systems. This may be exacerbated by the radar’s flightpath during the synthetic aperture data collection. It might also be exploited for target height estimation and offer insight to other height estimation techniques.
