High Resolution Synthetic Aperture Radar Based on Multiple Reflectarray Apertures

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

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.

This paper presents a method for the detection of roads in high resolution Synthetic Aperture Radar (SAR) images using an Ant Colony Algorithm (ACA). Roads in a high resolution SAR image can be modeled as continuously straight line segments of roadsides that possess width. In our method, line segments which represent the candidate positions for roadsides are first extracted from the image using a line segments extractor, and next the roadsides are accurately detected by grouping those line segments. For this purpose, we develop a method based on an ACA. We combine perceptual grouping factors with it and try to reduce its overall computational cost by a region growing method. In this process, a selected initial seed is grown into a finally grouped segment by the iterated ACA process, which considers segments only in a search region. Finally to detect roadsides as smooth curves, we introduce the photometric constraints in ant colony algorithm as external energy in a modified snake model to extract geometric roadsides model. We applied our method to some parts of TerraSAR-x images that have a resolution of about 1 m. The experimental results show that our method can accurately detect roadsides from high resolution SAR images.

Data fusion technique is an efficient way to benefit multi-source, multi-platform, and multi-angle remotely sensed information. Optical imagery and SAR (synthetic aperture radar) data are complementary in terms of capability of data acquisition and image characteristics. With their different capability and their unique information content respectively, fusion of high resolution SAR and optical multi-spectral imagery can improve the classification accuracy in land use. Texture information plays an important role for class discrimination especially in SAR imagery for its backscatter is sensitive to the type, orientation, homogeneity and spatial relationship of ground objects. In order to take full advantage of multi-source remotely sensed data and combine different features of them, this paper put forward a data fusion method for high spatial resolution remotely sensed data based on texture analysis. Texture features of high resolution SAR imagery were extracted using GLCM (Grey Level Co-occurrence Matrix) method. The texture features were detected in 0°, 45°, 90° and 135° four directions, and the moving window size of 3×3, 5×5, to 31×31, 41×41, 51×51, and 61×61 were tested to analyze the influences among them. The selected texture features were added with SAR data to make classification next. Both the two imagery were classified using an object-based and rule-based approach. Then, a decision level fusion was implemented and the accuracy of classification result was improved from 78.7% and 83.0% to 88.8%.

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.

High resolution Synthetic Aperture Radar (SAR) sensor, which delivers images with metric or sub-metric resolution, makes it possible to extract detailed urban information. An effective method for extracting building unit number information from high resolution SAR images is proposed. In this method, a combination of intensity threshold and morphological operations are firstly used to detect buildings in SAR imagery. Then a regression function that describes the obvious correlativity existed between the features of the detected bright patch and the real number of buildings in the bright patch are modeled to predict the building number information in any region. The experiment on a TerraSAR_X image covering part of Wuhan city of China with spatial resolution 1.25×1.25m per pixel shows that the proposed method can get much more accurate building unit number information than using the number of the detected bright patches as the number of building units directly.

Ship classification has long been a challenging task in synthetic aperture radar (SAR) applications. With the improvement of SAR resolution, it has gradually become possible based on high-resolution SAR images. This letter presents a joint feature representation method for characterizing the superstructure scattering features of merchant ships, and then a hierarchical scheme for classifying merchant ships in high-resolution SAR images based on integrated macro and detailed superstructure features is proposed. Comprehensive experiment on three types of high-resolution SAR samples demonstrates that the proposed features and method have the ability to improve the performance of merchant ship classification in SAR images.

In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to acquire azimuth high geometric resolution, and its two-dimensional (2D) impulse response with the low SLL is usually obtained from the 2D weighted power spectral density (PSD) by the selected weighting window function. However, this results in the SNR reduction due to 2D amplitude window weighting. In this paper, the staring spotlight SAR with nonlinear frequency modulation (NLFM) signal and azimuth non-uniform sampling (ANUS) is proposed to obtain high geometric resolution SAR images with the low SLL and almost without any SNR reduction. The NLFM signal obtains non-equal interval frequency sampling points under uniform time sampling by adjusting the instantaneous chirp rate. Its corresponding PSD is similar to the weighting window function, and its pulse compression result without amplitude window weighting has low sidelobes. To obtain a similar Doppler frequency distribution for low sidelobe imaging in azimuth, the received SAR echoes are designed to be non-uniformly sampled in azimuth, in which the sampling sequence is dense in middle and sparse in both ends, and azimuth compression result with window weighting would also have low sidelobes. According to the echo model of the proposed imaging mode, both the back projection algorithm (BPA) and range migration algorithm (RMA) are modified and presented to handle the raw data of the proposed imaging mode. Both imaging results on simulated targets and experimental real SAR data processing results of a ground-based radar validate the proposed low sidelobe imaging mode.

Archaeological prospecting is pivotal to understanding human civilization’s development and past human–environment interactions (PHEIs). Synthetic aperture radar (SAR), a unique active remote sensing (RS) technology capable of penetrating vegetation and dry soil, has become an indispensable tool for large-scale archaeological surveys. This review, encompassing 45 years of progress (1980–2024), systematically chronicles SAR’s evolution—a field we term <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SARchaeology</i>—from pioneering L-band discoveries to sophisticated modern techniques like high-resolution SAR (HiSAR), interferometric SAR (InSAR), polarimetric SAR (PolSAR), and multitemporal SAR analysis (MtSAR). These methods have enabled unprecedented discoveries of hidden landscapes, particularly in areas obscured by dense cover or aridity. However, SARchaeology grapples with inherent limitations we define as the “SAR gap,” driven by technical complexity and, critically, the difficulty in achieving adequate signal-to-noise ratios (SNR) for small low-backscatter archaeological targets. Traditional challenges like speckle noise, cost, and interpretation ambiguity are compounded by this fundamental issue. To bridge this gap, we propose an integrated framework of five strategic frontiers: 1) leveraging innovative long-wavelength SAR technologies (e.g., the P band) for deep penetration; 2) establishing collaborative space–air–ground networks; 3) integrating SAR with complementary RS techniques; 4) implementing artificial intelligence (AI) and machine learning (ML) for noise suppression and weak signal feature extraction; and 5) developing explainable SARchaeology (X-SARchaeology). This framework provides a definitive road map for the systematic exploration of buried civilizations in hyper-arid regions (e.g., the Silk Road and ancient Mesopotamia). By focusing on these solutions, SARchaeology is poised to transcend conventional limitations and illuminate much more of human history before its Golden Jubilee.

The new generation of high resolution Synthetic Aperture Radar (SAR) sensors provides a new "light" in the imaging and monitoring of built areas and structures: Ground structures can be now monitored to an unprecedented accuracy. Particularly the use of higher frequency enhances the sensitivity of the system even to minute changes, such as thermal dilations. By extending multitemporal differential interferometry SAR processing chains, in our case based on the multidimensional imaging approach, an additional parameter related to temperature at acquisition instants can be accurately estimated. In this work we investigate the application of the multi-dimensional SAR imaging technique to very high resolution TerraSAR-X data acquired over the city of Potenza (south Italy), with a particular attention to Musmeci bridge. Results demonstrate the potential of this technology to accurately monitor even minute deformations of the bridge, such as those caused by thermal dilations.

As FPGA device sizes increase, they offer greater opportunities for on-site data processing, which is potentially useful for reducing the transmission requirements for applications with large data sets. For satellite applications, unlike ASICs, designers also benefit from an FPGA's ability to be reprogrammed to update functionality over the lifetime of a satellite (15+ years) and a mission (often 5+ years), while having significantly lower power costs than GPGPUs or high performance processors. This paper presents the first custom, fully pipelined, adaptable framework for multi-beam Scan Mode Synthetic Aperture Radar (SAR), the only 24/7 remote sensing imaging system that is capable of producing high-resolution global images in any weather conditions. As high resolution SAR or even low-resolution global-coverage generates on the order of hundreds of Megabytes of raw data per second, onboard SAR processing would reduce this transmitted data by orders of magnitude. Our Scan-mode SAR processor is scalable to different bit-widths and frame-sizes. We are able to process 81×730 frames of 8-bit I-Q channels from each scan (1.4 MB) in less than 1.5 ms, approximately 102 times faster than a corresponding estimated C solution leveraging the Intel Integrated Performance Primitives, and 150 times faster than the corresponding fully vectorized software solution run in MATLAB (6-core, 3.5 Ghz CPU).

Current space borne synthetic aperture radar (SAR) systems are able to provide users with high resolution image data of around one meter. Focusing on systems operating in the X-band, this value is not the end of possible improvements in resolution. There still lies a great potential in an increase of bandwidth of the radar signal itself and also in a significant enlargement of the synthetic aperture. From the technical point of view this certainly is a challenge, but could be possible for future space borne SAR missions already with current state of the art hardware. As a matter of proof TerraSAR-X introduces a new staring spotlight image product that significantly improves the azimuthal resolution to around a quarter of a meter. The technical realization of the very high resolution SAR system is not the only obstacle to overcome. Especially the increase of Doppler bandwidth along the synthetic aperture requests special treatment and considerations in system design from a signal processing’s point of view. Challenges like orbital accuracy, tropospheric effects, approximations in SAR processing methods and depth-of-focus issues have to be addressed. In this paper, most of these challenges are studied separately by performing parametric simulations for single point targets and also for complex signatures of an airplane. A comparable SAR parameter set as used by the high resolution sliding spotlight mode and the new staring spotlight mode of TerraSAR-X are used for simulation.

Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships quickly and accurately. To address these issues above, a novel YOLO-based arbitrary-oriented SAR ship detector using bi-directional feature fusion and angular classification (BiFA-YOLO) is proposed in this article. First of all, a novel bi-directional feature fusion module (Bi-DFFM) tailored to SAR ship detection is applied to the YOLO framework. This module can efficiently aggregate multi-scale features through bi-directional (top-down and bottom-up) information interaction, which is helpful for detecting multi-scale ships. Secondly, to effectively detect arbitrary-oriented and densely arranged ships in HR SAR images, we add an angular classification structure to the head network. This structure is conducive to accurately obtaining ships’ angle information without the problem of boundary discontinuity and complicated parameter regression. Meanwhile, in BiFA-YOLO, a random rotation mosaic data augmentation method is employed to suppress the impact of angle imbalance. Compared with other conventional data augmentation methods, the proposed method can better improve detection performance of arbitrary-oriented ships. Finally, we conduct extensive experiments on the SAR ship detection dataset (SSDD) and large-scene HR SAR images from GF-3 satellite to verify our method. The proposed method can reach the detection performance with precision = 94.85%, recall = 93.97%, average precision = 93.90%, and F1-score = 0.9441 on SSDD. The detection speed of our method is approximately 13.3 ms per 512 × 512 image. In addition, comparison experiments with other deep learning-based methods and verification experiments on large-scene HR SAR images demonstrate that our method shows strong robustness and adaptability.

In high-resolution (HR) and very-high resolution (VHR) synthetic aperture radar (SAR) images, focus is now on the patch-oriented image categorization in contrast to the pixel-based classification in low-resolution SAR images. SAR image categorization requires the generation of a compact feature descriptor that accurately defines the content of the image patch under consideration. In this paper we propose a parametric feature descriptor generated on the complex-valued SAR image within a transformation space. The fractional Fourier transform (FrFT), has been considered to transform the image pixels of the single-look complex (SLC) SAR images in order to obtain a simpler statistical response. The real and imaginary components of the complex-valued FrFT coefficients have been modelled with generalized Gaussian distribution (GGD). The proposed feature descriptor is compared with a Wavelet-decomposition-based parametric feature descriptor; and with the FrFT-based and Gabor-filter-bank-based non-parametric feature descriptors. Categorization accuracy enhancement is demonstrated over several categories comprising of natural topologies. The experimental database consists of 2000 image patches (of size 200 × 200 pixels) extracted from SLC HR TerraSAR-X scenes.

The application of high-resolution synthetic aperture radar (SAR) images from aerial and satellite sensors challenges the researchers for new and effective interpretation tools. Although most of the main axes in the road network may be detected by a skilled human observer looking for dark or bright linear structures, automatic detection remains a difficult task. In this paper, we proposed a new simple approach to extract main road network in high-resolution SAR images automatically. The approach is based on three steps. The first step is pre-filtering the initial SAR images by using a two-way thresholding process in order to discard uninteresting parts of image. In the second step, the results of the first step are input and use the Hough transform to identify the roads respectively. The last step is based on a feature fusion technique. The road networks detected are combined with the fusion operators. We show the improved results from some examples. It is proved that this approach is effective for the straight highways in high-resolution images