Radar Images Research Articles

MT-InSAR Optimisation for Structural Health Monitoring

The implementation of effective and sustainable Structural Health Monitoring (SHM) systems for the evaluation of infrastructure conditions is critical to address the deterioration and damage experienced by structures worldwide. Given the vast number of structures involved, resorting to traditional in-situ visual inspections and data gathering methods is becoming increasingly unfeasible. Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has recently gained attention as a viable solution for long-term SHM. This remote sensing technique combines multiple satellite radar images to measure changes in the Earth’s surface over time. Unlike conventional techniques, MT-InSAR does not require in-situ installations and offers extensive coverage, enabling observations across diverse location and structures. However, the applicability of MT-InSAR monitoring depends on the relatively unpredictable distribution and location of permanent scatterers (PSs), which are influenced by surface characteristics and vegetation changes. Evaluating the reliability and capacity of MT-InSAR is therefore crucial to enhance its effectiveness in assessing the location and extent of structural damage. In this study, we present an effective approach to determine the optimal number and position of PSs for detecting different structural damage mechanisms. The approach is exemplified through a case study of a quay wall in Amsterdam, with data inputs simulated using the Finite Element Method. The proposed method has the potential to evaluate the feasibility of MT-InSAR for a broader range of scenarios, enabling to detect specific structural conditions.

Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks.

Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method.

Error analysis and correction of atmospheric disturbance for interferometric imaging radar altimeter

The interferometric imaging radar altimeter (InIRA) is a newly developed microwave remote sensing system in recent years. It is of great significance to analyze the main sources of its height measurement error through simulation, and to develop the corresponding image phase error correction methods. This can provide basis and reference for the subsequent correction of the real images phase error. In this study, according to the interaction principle between atmospheric interference and InIRA radar electromagnetic waves, the influence of background ionosphere and background troposphere on height measurement error is analyzed by introducing ionosphere and troposphere delays into echo signals respectively. The impact of ionospheric scintillation and atmospheric turbulence on the height measurement error is analyzed by building multi-layer phase screen models. Then according to the analysis results, a phase error correction method for InIRA images combined by the improved fruit fly optimization algorithm (IFOA) and the joint minimum entropy criterion (JMEC) is proposed. The image is divided into several sub-images with negligible spatial variation of phase error according to the principle of first range and then azimuth. IFOA is used to search the optimal phase compensation values iteratively to compensate the phase of all sub-images. When the joint entropy of the sub-images reaches the minimum value, the phase error correction of the whole InIRA image is completed. Experimental results show that the proposed method can correct the phase of InIRA images with inconspicuous features and polluted by weak ionospheric scintillation and atmospheric turbulence relatively quickly. After correction, the height measurement error can be reduced from decimeter level to centimeter level. The correction accuracy and efficiency of the proposed method are superior to those of the method combined by particle swarm optimization algorithm and joint minimum entropy criterion (PSO-JMEC), the method combined by genetic algorithm and joint minimum entropy criterion (GA-JMEC), the method combined by phase gradient autofocus and Map Drift (PGA-MD), and improved PGA-MD algorithm.

Vulnerabilities and exposure of recent informal urban areas in Lima, Peru

Urban areas are experiencing rapid growth, accompanied by significant disorder in Lima Metropolitan area and many other cities in South America. Due to decades of uncontrolled construction practices, it is imperative to identify and better understand the types of informalities prevalent in these recent urban areas. Addressing this lack of information is crucial for implementing appropriate countermeasures and developing new policies that benefit the communities residing in such areas. It is worth noting that understanding disaster risk aligns with the first priority of the Sendai Framework for Disaster Risk Reduction. In this study, we propose the use of radar satellite imagery recorded by the Sentinel-1 constellation since 2017 to identify clusters of urban growth in Lima Metropolitan area. Then, the informal urban clusters can be depicted by visual inspection of the last recorded high-resolution optical image. With good spatial and temporal resolution, we identified 25 clusters informal areas. Among our findings, we observed that several of these clusters are situated in landfills comprised of construction and other waste, increasing their vulnerability to debris flow, landslides, and earthquakes. Additionally, we noted that some new urban areas mainly consist of temporarily empty houses, highlighting the feasibility of implementing countermeasures, such as relocations, in the early stages of informal occupation. These results underscore the significant contribution of satellite radar imagery in identifying recent informal urban areas.

Advanced Method for Improving Marine Target Tracking Based on Multiple-Plot Processing of Radar Images

Advancements in technology have led to the development of high-resolution radars that provide highly detailed images of targets over a wide field of view. These radar images can significantly improve filtering and tracking accuracy, especially in marine environments. However, traditional methods like the binary and barycentric methods are inadequate, as they do not capture critical information for tracking targets, such as direction. Therefore, in this article, a new method for improving the estimation of target characteristics to improve tracking accuracy based on the processing of high-resolution radar images is proposed. The proposed method consists of three modules. Firstly, the radar images of the target are decomposed into layers to determine local maximum regions and to estimate target characteristics such as reflected energy and area and the centroids of plots. In the second module, the plots are grouped using a fuzzy logic system. The inputs of the fuzzy logic system include the above-estimated parameters of the targets. The output is the chance that the plot is at the center of the target. The plots with the highest chances are considered target centers, and the other plots are grouped into their respective target. At the end, the true target center is recalculated. This process is called modified fuzzy C-means (FCM-M). In the last stage, the estimated target center coordinates are fed into a Kalman filter (KF) to solve filtering and tracking problems. The performance is evaluated using a measured radar dataset. The experimental results show that the proposed method performs better than traditional methods based on binary image processing. Additionally, the proposed method offers extra information about the targets, such as their direction, the energy of each reflected part, and the area, which traditional methods does not provide.

Analysis of the peculiarities of the method of combining an electronic cartographic navigation information system with a ship's radar station

The article analyses the importance of radar image overlay on the screen of an electronic charting navigation information system (ECNIS) for improving safety and efficiency of navigation. Based on a review of scientific publications and professional literature, the article discusses the technical aspects of the overlay function, its advantages and limitations, and the impact on the decision-making process of a navigator. The overlay of radar information on an electronic navigation chart ensures the integration of planned and actual data on the environment, which contributes to the increase of situational awareness of the navigator. The combination of the systems makes it possible to visually compare radar targets with cartographic objects, clarify the vessel's location, control drift and orientation, and detect navigation hazards. The overlay function is especially valuable when sailing in confined waters and in conditions of limited visibility. At the same time, the use of overlay technology is associated with a number of technical difficulties and limitations related to ensuring the accuracy of overlap, harmonisation of data formats, and synchronisation of information updates. Errors in the operation of positioning systems, direction guidance, and cartography errors can lead to inconsistencies in image layers and incorrect interpretation of the navigation situation. The issues of equipment reliability, display parameters settings, competence and training of navigators are of critical importance. It is emphasised that radar image overlay on ECDIS is an effective tool for improving safety of navigation, but it does not replace traditional navigation practices and does not eliminate the need for critical evaluation of data. Over-reliance on technology, ignoring its limitations and disregard for the principles of good maritime practice can lead to erroneous decisions and have a negative impact on safety. Further prospects for the development of technology can be associated with the improvement of information processing algorithms, optimisation of human-machine interaction, and the development of training methods. The article emphasises the need for an integrated approach combining the improvement of technical systems and the human factor to fully realise the potential of the overlay function in ensuring safety of navigation. Only a balanced use of technological innovations and traditional navigational practices can guarantee the reliability and efficiency of modern navigation

Crop mapping based on time-series synthetic aperture radar images using deep semantic segmentation model

Crop mapping based on time-series synthetic aperture radar images using deep semantic segmentation model

Experimental evaluation of a multi-installment scheduling strategy based on divisible load paradigm for SAR image reconstruction on a distributed computing infrastructure

Radar loads, especially Synthetic Aperture Radar (SAR) image reconstruction loads use a large volume of data collected from satellites to create a high-resolution image of the earth. To design near-real-time applications that utilise SAR data, speeding up the image reconstruction algorithm is imperative. This can be achieved by deploying a set of distributed computing infrastructures connected through a network. Scheduling such complex and large divisible loads on a distributed platform can be designed using the Divisible Load Theory (DLT) framework. We performed distributed SAR image reconstruction experiments using the SLURM library on a cloud virtual machine network using two scheduling strategies, namely the Multi-Installment Scheduling with Result Retrieval (MIS-RR) strategy and the traditional EQual-partitioning Strategy (EQS). The DLT model proposed in the MIS-RR strategy is incorporated to make the load divisible. Based on the experimental results and performance analysis carried out using different pixel lengths, pulse set sizes, and the number of virtual machines, we observe that the time performance of MIS-RR is much superior to that of EQS. Hence the MIS-RR strategy is of practical significance in reducing the overall processing time, and cost, and in improving the utilisation of the compute infrastructure. Furthermore, we note that the DLT-based theoretical analysis of MIS-RR coincides well with the experimental data, demonstrating the relevance of DLT in the real world.

A quest for precipitation attractors in weather radar archives

Abstract. Archives of composite weather radar images represent an invaluable resource to study the predictability of precipitation. In this paper, we compare two distinct approaches to construct empirical low-dimensional attractors from radar precipitation fields. In the first approach, the phase space variables of the attractor are defined using the domain-scale statistics of precipitation fields, such as the mean precipitation, fraction of rain, and spatial and temporal correlations. The second type of attractor considers the spatial distribution of precipitation and is built by principal component analysis (PCA). For both attractors, we investigate the density of trajectories in phase space, growth of errors from analogue states, and fractal properties. To represent different scales and climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000×4000 km2, 21 years) and the Swiss Alpine region (≈500×500 km2, 6 years).

ANADEM: A Digital Terrain Model for South America

Digital elevation models (DEMs) have a wide range of applications and play a crucial role in many studies. Numerous public DEMs, frequently acquired using radar and optical satellite imagery, are currently available; however, DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation. In this study, we developed a digital terrain model for South America using a novel methodology to remove vegetation bias in the Copernicus DEM GLO-30 (COPDEM) model using machine learning, Global Ecosystem Dynamics Investigation (GEDI) elevation data, and multispectral remote sensing products. Our results indicate considerable improvements compared to COPDEM in representing terrain elevation, reducing average errors (BIAS) from 9.6 m to 1.5 m. Furthermore, we evaluated our product (ANADEM) by comparison with other global DEMs, obtaining more accurate results for different conditions of vegetation fraction cover and land use. As a publicly available and open-source dataset, ANADEM will play a crucial role in advancing studies that demand accurate terrain elevation representations at large scales.

Enhanced synthetic aperture radar image autofocus and classification using 2D SARNet framework

Enhanced synthetic aperture radar image autofocus and classification using 2D SARNet framework

Artificial Ineligence for Synthetic Aperture Radar Image Processing

Artificial Ineligence for Synthetic Aperture Radar Image Processing

High-precision flood detection and mapping via multi-temporal SAR change analysis with semantic token-based transformer

Flood detection in crisis and disaster management is significantly facilitated by the analysis of synthetic aperture radar (SAR) imagery. Traditional flood detection techniques focus more on SAR image pairs than on the optical level. However, the distinctive characteristics of SAR images, characterized by limited visual information, pervasive speckle noise, and analogous backscatter signals, present formidable obstacles to accurately identifying water bodies and extracting change features. Consequently, the performance of existing methods remains unsatisfactory. This paper addresses this challenge by focusing on disparities between SAR image pairs and introducing a pioneering semantic token-based transformer network, denoted as SemT-Former, to enhance flood detection accuracy. SemT-Former operates by prioritizing changes of interest rather than fully comprehending the entire image scene. This is achieved through the integration of temporal-wise feature representation and the introduction of a class token to capture high-level segmentation associated with changes in water bodies. These innovations augment the model’s capacity to discriminate between genuine changes in water bodies and spurious changes induced by similar signals or speckle noise. The effectiveness of SemT-Former is evaluated through a case study in Khartoum, Sudan, focusing on flood detection and the estimation of damaged farmland near river confluences. Experimental results demonstrate that SemT-Former outperforms existing methods, exhibiting a 90.6% improvement in F1-score and an 88.5% enhancement in IoU. This underscores SemT-Former as a promising solution for precise and effective flood mapping from SAR images.

REN-GAN: Generative adversarial network-driven rebar clutter elimination network in GPR image for tunnel defect identification

In ground penetrating radar (GPR) images, the signals from reinforcing steel bars (rebars) can significantly obscure the signals of void defects beneath them, which hinders defect detection in tunnel linings. This study introduces a novel, end-to-end unsupervised deep learning-based network that is trained on unpaired GPR images. The primary objective is to suppress the rebar signal and enhance the accuracy of void defect identification in recorded GPR B-Scan images. The network incorporates an autoencoder generator with feature attention and feature mix-up modules, designed to recover textural information and generate improved images devoid of rebar interference. Crucially, this network leverages a strategy of comparing images in feature space, rather than pixel space. The features extracted by the autoencoder are constrained by cycle perceptual consistency loss, derived from a pretrained VGG19 network, to ensure fidelity and perceptual accuracy. Furthermore, this study introduces an enhanced Faster-RCNN network, specifically improved through a more robust feature extraction module, for detecting void defects beneath rebars. The effectiveness of the proposed method has been thoroughly validated using both synthetic and real-world data sets. The results indicate that our method not only surpasses other unsupervised techniques in performance but also effectively diminishes rebar signals and accurately reconstructs and identify void defect signals.

Analysis of Superresolution Methods in the Application to the Subsurface Sounding Radar for Detecting Hidden Objects

Adaptive methods have been developed for generating a radar image of a probed surface area. A block diagram of a subsurface sounding radar is described. The results of modeling and experimental studies are presented. Their use makes it possible to develop a subsurface sensing radar capable of effectively identifying hidden objects in concrete walls, as well as detecting cracks, voids and defects in the walls of buildings and structures, snow debris and avalanches, underground communications and organic bodies in the ground.

LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images

LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images

Automatic target detection and recognition of military vehicles in synthetic aperture radar images is fostered by optimizing VGG-googLeNet with the giraffe kicking optimization algorithm

Automatic target detection and recognition of military vehicles in synthetic aperture radar images is fostered by optimizing VGG-googLeNet with the giraffe kicking optimization algorithm

FPGA-Based Acceleration of Polar-Format Algorithm for Video Synthetic-Aperture Radar Imaging

This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the processor design was based on a field-programmable gate array (FPGA), and the implementation results are presented. The proposed PFA-based SAR processor consists of both an interpolation unit and a fast Fourier transform (FFT) unit. The interpolation unit uses linear interpolation for high speed while occupying a small space. In addition, the memory transfer is minimized through optimized operations using SAR system parameters. The FFT unit uses a base-4 systolic array architecture, chosen from among various fast parallel structures, to maximize the processing speed. Each unit is designed as a reusable block (IP core) to support reconfigurability and is interconnected using the advanced extensible interface (AXI) bus. The proposed PFA-based SAR processor was designed using Verilog-HDL and implemented on a Xilinx UltraScale+ MPSoC FPGA platform. It generates an image 2048 × 2048 pixels in size within 0.766 s, which is 44.862 times faster than that achieved by the ARM Cortex-A53 microprocessor. The speed-to-area ratio normalized by the number of resources shows that it achieves a higher speed at lower power consumption than previous studies.

A Visible and Synthetic Aperture Radar Image Fusion Algorithm Based on a Transformer and a Convolutional Neural Network

For visible and Synthetic Aperture Radar (SAR) image fusion, this paper proposes a visible and SAR image fusion algorithm (TCFuse) based on a Transformer and a Convolutional Neural Network (CNN). Firstly, in this paper, the Restormer Block is used to extract cross-modal shallow features. Then, we introduce an improved Transformer–CNN Feature Extractor (TCFE) with a two-branch residual structure. This includes a Transformer branch that introduces the Lite Transformer (LT) and DropKey for extracting global features and a CNN branch that introduces the Convolutional Block Attention Module (CBAM) for extracting local features. Finally, the fused image is output based on global features extracted by the Transformer branch and local features extracted by the CNN branch. The experiments show that the algorithm proposed in this paper can effectively achieve the extraction and fusion of global and local features of visible and SAR images, so that high-quality visible and SAR fusion images can be obtained.

Polarization-insensitive, wideband reflective metasurface generating orbital angular momentum vortex beam

In this paper, a low-profile polarization-insensitive reflective metasurface is proposed to generate high gain and wideband orbital angular momentum (OAM) in X-band (in the frequency range of 8−12GHz). By adjusting a subwavelength pseudo-screw-shaped meta-atom’s size the required phase compensation has been achieved for designing a reflectarray antenna at 10GHz. The theoretical analyses and simulation results confirm that the proposed structure can effectively generate the first-, second-, third-, and fourth-order OAM vortex beams at the operating frequency. To verify the bandwidth of the suggested metasurface, the structure is implemented for first-order OAM mode generation at various frequencies. The outcomes of investigations show the OAM operational bandwidth and 1.5-dB gain bandwidth are attained to be 40 %, and 27.5 %, respectively. Meanwhile, approximately 14.35 % reflection efficiency was procured for the overall design at the operating frequency of 10GHz. The numerical aperture of the reflectarray metasurface is also conceptually defined and calculated for varying positions of the center-feed antenna. The admitted performance and substantial characteristics of the proposed OAM reflectarray antenna may make it a useful candidate for radar imaging and detection applications as well as broadband microwave communications.