High-resolution Synthetic Aperture Research Articles

Investigating the Structural Health of High-Rise Buildings and Its Influencing Factors Using Sentinel-1 Synthetic Aperture Radar Imagery: A Case Study of the Guangzhou–Foshan Metropolitan Area

Urban growth is increasingly shifting from horizontal expansion to vertical development, resulting in skylines dominated by high-rise buildings. The post-construction operations and maintenance of these buildings are critical, requiring regular structural health monitoring (SHM) to proactively identify and address potential safety concerns. Interferometric synthetic aperture radar (InSAR) has proven effective for monitoring building safety, but most studies rely on high-resolution synthetic aperture radar (SAR) images. The high cost and limited coverage of these images restrict their use for large-scale monitoring. Sentinel-1 medium-resolution SAR images, which are freely available and offer broad coverage, make large-scale SHM more feasible. However, studies on the use of Sentinel-1 SAR images for structural health monitoring, especially at large spatial scales, remain limited. To address this gap, in this study, Sentinel-1 SAR images and PS-InSAR technology are proposed for performing a comprehensive structural safety assessment of super high-rise buildings in the Guangzhou–Foshan Metropolitan Area (GFMA) and for analyzing the influencing factors. Our assessment shows that while the overall structural safety of these buildings is satisfactory, certain areas, including Pearl River New Town, central Huadu district in Guangzhou, and southeastern Shunde district in Foshan, exhibit suboptimal safety conditions. We verified these findings using GNSS data and on-site investigations, confirming that Sentinel-1 SAR imagery offers reliable accuracy for monitoring building structural health. Furthermore, we identified factors such as settlement in soft soil layers, the construction of surrounding (underground) infrastructure, and building aging, which could potentially impact building structural safety. The results demonstrate that Sentinel-1 SAR images provide a reliable, rapid, and cost-effective method for the large-scale monitoring of building stability, enhancing our understanding of the underlying mechanisms and informing strategies to prevent potential safety crises, and also ensuring the sustainable development of society.

Atmospheric fronts detection and cold/warm type determination based on wind field retrieval from SAR imagery

ABSTRACT Atmospheric fronts are one of the important atmospheric phenomena in the marine atmospheric boundary layer. Currently, the detection of atmospheric fronts relies on low-resolution global Numerical Weather Prediction (NWP) gridded datasets, which cannot analyse the fine-scale structural features of atmospheric fronts. High-resolution Synthetic Aperture Radar (SAR) imagery can be used to study the structure and characteristics of atmospheric fronts, but the potential of SAR-retrieved high-resolution wind field for atmospheric fronts detection has not yet been fully exploited. In this paper, we have presented for the first time a method for atmospheric fronts detection and cold/warm type determination based on sea surface wind field retrieval from SAR imagery. The proposed method was experimentally validated using Sentinel-1 SAR images, and the detection and determination results were compared with those from classical methods and the surface maps analyses datasets. The results showed good consistency, demonstrating the effectiveness of the proposed method.

The High-Resolution Synthetic Aperture Radar System and Signal Processing Techniques: Current progress and future prospects [Review Papers

The High-Resolution Synthetic Aperture Radar System and Signal Processing Techniques: Current progress and future prospects [Review Papers

Potential of the upcoming Biomass P-band radar mission for digital terrain modelling beneath dense tropical forests: first accuracy assessment

Abstract. P-band radar surveys can be used to produce digital elevation models (DEMs) at ground level in dense tropical forest areas. However, the previous performance studies were carried out on DEMs derived from high-resolution synthetic aperture radar (SAR) acquisitions, while the upcoming Biomass space mission should provide DEMs at a coarser resolution due to a frequency bandwidth of 6 MHz in range. The aim of this study is to predict the quality of the DEM products derived from Biomass data through dense forest. A DEM was produced in a slant range geometry at a 15-m mesh size using SAR tomography. It was compared to the reference LiDAR survey in terms of elevation and slope, and the statistical distribution of the corresponding error was analysed with regards to the radar off-nadir angle and to the local slope and aspect. The results show that the DEM elevation has no systematic error despite the presence of forest, and it has an RMS error of 3 meters. The DEM slope has a mean error of 2° and an RMS error of 6°. The behaviour of these errors for different terrain slopes and aspects will allow to predict the quality of Biomass DEMs on a variety of terrestrial landscapes.

Vehicle Localization Method in Complex SAR Images Based on Feature Reconstruction and Aggregation.

Due to the small size of vehicle targets, complex background environments, and the discrete scattering characteristics of high-resolution synthetic aperture radar (SAR) images, existing deep learning networks face challenges in extracting high-quality vehicle features from SAR images, which impacts vehicle localization accuracy. To address this issue, this paper proposes a vehicle localization method for SAR images based on feature reconstruction and aggregation with rotating boxes. Specifically, our method first employs a backbone network that integrates the space-channel reconfiguration module (SCRM), which contains spatial and channel attention mechanisms specifically designed for SAR images to extract features. The network then connects a progressive cross-fusion mechanism (PCFM) that effectively combines multi-view features from different feature layers, enhancing the information content of feature maps and improving feature representation quality. Finally, these features containing a large receptive field region and enhanced rich contextual information are input into a rotating box vehicle detection head, which effectively reduces false alarms and missed detections. Experiments on a complex scene SAR image vehicle dataset demonstrate that the proposed method significantly improves vehicle localization accuracy. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method.

Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission.

In response to the conflicting demands between real-time satellite communication and high-resolution synthetic aperture radar (SAR) imaging, we propose a method that aligns the data transmission rate with the imaging data volume. This approach balances SAR performance with the requirements for real-time data transmission. To meet the need for mobile user terminals to access real-time SAR imagery data of their surroundings without depending on large traditional ground data transmission stations, we developed an application system based on filter bank multicarrier offset quadrature amplitude modulation (FBMC-OQAM). To address the interference problem with SAR signals' transmission and reception, we developed a signal sequence based on spaceborne SAR echo and data transmission and reception. This system enables SAR and data transmission signals to share the same frequency band, radio frequency transmission system, and antenna, creating an integrated sensing and communication system. Simulation experiments showed that, compared to the equal power allocation scheme for subcarriers, the echo image signal-to-noise ratio (SNR) improved by 2.79 dB and the data transmission rate increased by 24.075 Mbps.

A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery

Seasonal snow plays a crucial role in society and understanding trends in snow depth and mass is essential for making informed decisions about water resources and adaptation to climate change. However, quantifying snow depth in remote, mountainous areas with complex topography remains a significant challenge. The increasing availability of high-resolution synthetic aperture radar (SAR) observations from active microwave satellites has prompted opportunistic use of the data to retrieve snow depth remotely across large spatial and frequent temporal scales and at a high spatial resolution. Nevertheless, these novel SAR-based snow depth retrieval methods face their own set of limitations, including challenges for shallow snowpacks, high vegetation cover, and wet snow conditions. In response, here we introduce a machine learning approach to enhance SAR-based snow depth estimation over the European Alps. By integrating Sentinel-1 SAR imagery, optical snow cover observations, and topographic, forest cover and snow class information, our machine learning retrieval method more accurately estimates snow depth at independent in-situ measurement sites than current methods. Further, our method provides estimates at 100 m horizontal resolution and is capable of better capturing local-scale topography-driven snow depth variability. Through detailed feature importance analysis, we identify optimal conditions for SAR data utilization, thereby providing insight into future use of C-band SAR for snow depth retrieval.

Matching UAV-Based Simulated High-Resolution SAR and Real SAR Images

Abstract. Our experiment focuses on generating simulated high-resolution (HR) Synthetic Aperture Radar (SAR) images from the photogrammetric 3D model derived from Unmanned Aerial Vehicle (UAV) imagery. The goal is to match these simulated images with real SAR data of complex scenes containing vegetation, buildings, roads, and the construction of receiving antennas. Simulated SAR reflectivity map was based on ray tracing techniques.Two feature matching algorithms, namely Scale-Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) were employed for co-registration of the simulated to the real SAR images. The effectiveness of these algorithms is evaluated by their matching rates. The experiment results of the matching process highlight the superior performance of ORB over SIFT, attributed to its robustness against rotation variations and noise. ORB emerges as a versatile choice for high-resolution real and simulated SAR registration tasks.

A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic

In the mosaic creation of multiple high-resolution synthetic aperture radar (SAR) images, achieving an optimal seamline in overlapping areas is crucial for seamless and visually satisfactory results. Many existing seamline generation methods are designed primarily for optical remote sensing images, but due to the differing characteristics of SAR images and optical images, applying these methods directly to SAR images poses challenges in finding the optimal seamline. In response, this paper proposes a segmentation-based optimal seamline generation (SOSG) method for SAR image mosaics. The SOSG method involves a multi-step process. First, SAR image joint segmentation is performed within the overlapping areas. Subsequently, homogeneous areas are identified based on the segmentation results. Following this, a pixel cost matrix is constructed, incorporating homogeneous areas and intensity differences. Finally, the minimum path cost from the starting pixel to the end pixel is computed using the Dijkstra algorithm to determine the optimal path. To assess the feasibility and effectiveness of the proposed method, experiments are conducted using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets. The experimental results demonstrate that the proposed method yields seamless mosaic images when compared to other methods, while delivering satisfactory outcomes. This indicates the potential of the proposed method in addressing the unique challenges posed by SAR images and enhancing the quality of SAR image mosaics.

A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification

High-resolution synthetic aperture radars (SARs) are becoming an indispensable environmental monitoring system to capture the important geophysical phenomena on the earth and sea surface. However, there is a lack of comprehensive models that can orchestrate such large-scale datasets from numerous satellite missions such as GaoFen-3 and Sentinel-1. In addition, these SAR images of different ocean scenes need to convey a variety of high-level classification features in oceanic and atmospheric phenomena. In this study, we propose a multichannel neural network (MCNN) that supports oceanic SAR scene classification for limited oceanic data samples according to multi-feature fusion, data augmentation, and multichannel feature extraction. To exploit the multichannel semantics of SAR scenes, the multi-feature fusion module effectively combines and reshapes the spatiotemporal SAR images to preserve their structural properties. This fine-grained feature augmentation policy is extended to improve the data quality so that the classification model is less vulnerable to both small- and large-scale data. The multichannel feature extraction also aggregates different oceanic features convolutionally extracted from ocean SAR scenes to improve the classification accuracy of oceanic phenomena with different scales. Through extensive experimental analysis, our MCNN framework has demonstrated a commendable classification performance, achieving an average precision rate of 96%, an average recall rate of 95%, and an average F-score of 95% across ten distinct oceanic phenomena. Notably, it surpasses two state-of-the-art classification techniques, namely, AlexNet and CMwv, by margins of 23.7% and 18.3%, respectively.

Very High Resolution Synthetic Aperture Radar Systems and Imaging: A Review

Very High Resolution Synthetic Aperture Radar Systems and Imaging: A Review

Mapping Floods during Cloudy Weather Using Radar Satellite Images

Monitoring floods using optical satellite images in winter is challenging. Because of the thickness of clouds, the electromagnetic bands used by satellites are too narrow and the resulting signals are reflected into space. The purpose of this study is to assess the potential of implementing a high-resolution synthetic aperture radar system (SAR) for the purpose of mapping floods in Jordan while the weather is cloudy. TerraSAR-X satellite was utilized to acquire images of high resolution for flood monitoring. This research employed TerraSAR-X thresholding images to identify flood-prone areas and track floods over time using time-series images. It was possible to determine the places of the landscape that have been impacted by the flood and to establish the degree of damage by conducting an analysis of the changes that have occurred in the landscape. The accuracy of the study's findings was confirmed using backscatter texture analysis on the images, which revealed a small difference between thresholding and texture analysis approaches of less than 3% in the mapped test region's flood area. While monitoring floods, it is recommended to use TerraSAR-X images, particularly in conjunction with other data sources, such as weather predictions and hydrological models. KEYWORDS: Flood mapping, Polarimetric SAR, TerraSAR-X, Texture analysis, Thresholding, Interferometric SAR

A conventional and non-conventional analysis of SAR image despeckling technique

The importance of high-resolution Synthetic Aperture Radar (SAR) imaging is undeniable in applications for Earth monitoring since it offers useful data for analysis. Post-image processing methods including edge and object detection, segmentation, and speckle noise removal are important to performed to find the information of images. The key technique that makes images aesthetically pleasing and understandable is despeckling. The presented study assays the latest trends & state of the art techniques for SAR image despeckling to gauge the performance & agility of existing techniques by performing a contemporary combination of theoretical & experimental analysis. This study analyses various techniques used in literature. The quantitative & qualitative analysis is done using Peak Signal-to-Noise Ratio (PSNR) and Universal Image Quality Index (UIQI) indices to find the better approach

Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

AbstractOver half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.

A multi-scale algorithm for the NISAR mission high-resolution soil moisture product

This study proposes a multi-scale soil moisture algorithm for the upcoming NASA-ISRO SAR (NISAR) mission to estimate high-resolution (200 [m]) soil moisture (the water content of the soil). The algorithm takes advantage of the high-resolution (∼10 [m]) synthetic aperture radar (SAR) backscatter and coarse resolution modeled/reanalysis soil moisture products (∼ 9 [km]) to create a high-resolution (200 [m]) soil moisture product at a global extent. The end goal of the algorithm is to remove dependencies on any complex modeling, tedious retrieval steps, or multiple ancillary data needs, and subsequently decrease the degrees of freedom to achieve optimal accuracy in soil moisture retrievals. The use of modeled/reanalysis soil moisture products with high temporal resolution gives an added advantage in reducing the temporal mismatch between the two different inputs used in the algorithm. In this study, the proposed algorithm is tested using L-band UAVSAR backscatter (σ°) data and Advanced Land Observing Satellite −2 (ALOS-2) SAR σ° as a substitute for the NISAR L-band SAR observations. The algorithm uses the L-band SAR σ° to disaggregate coarse resolution (∼9 [km]) reanalysis soil moisture of the European Centre for Medium-Range Weather Forecast (ECMWF) to a high-resolution of ∼200 [m] soil moisture product. The potential of the algorithm is demonstrated over three sites in different hydroclimatic regions of the world, such as India, the USA, and Canada. The high-resolution soil moisture estimates were compared with the in-situ soil moisture measurements available for three sites (North India, Southern California, and Carman, Manitoba, Canada). In North India, in-situ measurements are from paddy crops with high vegetation water content, the unbiased root-mean-square-error (ubRMSE) for the high-resolution soil moisture retrievals was found to be 0.036 [m3/m3] with a bias of −0.051 [m3/m3]. For the southern California site, the validation statistics shows low ubRMSE of 0.027 [m3/m3] and a low bias of 0.016 [m3/m3]. At the Carman, Manitoba test site of Canada, where in-situ soil measurements were available for multiple crop types, the comparison shows that the ubRMSE for all the crop types lies below 0.05 [m3/m3] with an average bias of <0.07 [m3/m3]. The result confirms that the proposed algorithm meets the NISAR mission's accuracy goals, i.e., 0.06 [m3/m3] ubRMSE over areas with vegetation water content (VWC) below 5 [kg/m2]. Rigorous validation work will still need to be carried out in the future based on the availability of L-band SAR datasets and after the launch of the NISAR satellite.

A Marine Small-Targets Classification Algorithm Based on Improved Convolutional Neural Networks

Deep learning, especially convolutional neural network (CNN) techniques, has been shown to have superior performance in ship classification, as have small-target recognition studies in safety inspections of hydraulic structures such as ports and dams. High-resolution synthetic aperture radar (SAR)-based maritime ship classification plays an increasingly important role in marine surveillance, marine rescue, and maritime ship management. To improve ship classification accuracy and training efficiency, we proposed a CNN-based ship classification method. Firstly, the image characteristics of different ship structures and the materials of ship SAR images were analyzed. We then constructed a ship SAR image dataset and performed preprocessing operations such as averaging. Combined with a classic neural network structure, we created a new convolutional module, namely, the Inception-Residual Controller (IRC) module. A convolutional neural network was built based on the IRC module to extract image features and establish a ship classification model. Finally, we conducted simulation experiments for ship classification and analyzed the experimental results for comparison. The experimental results showed that the average accuracy of ship classification of the model in this paper reached 98.71%, which was approximately 3% more accurate than the traditional network model and approximately 1% more accurate compared with other recently improved models. The new module also performed well in evaluation metrics, such as the recall rate, with accurate classifications. The model could satisfactorily describe different ship types. Therefore, it could be applied to marine ship classification management with the possibility of being extended to hydraulic building target recognition tasks.

High-Resolution Synthetic Aperture Imaging Algorithm for Terahertz

The reconstruction and recovery of targets in the terahertz band has always been a direction for the improvement of synthetic aperture radar imaging algorithms. In order to improve the imaging resolution of the target, the scattering principle of electromagnetic waves to the target is analyzed. Through model establishment and formula derivation, the propagation process between the electromagnetic wave and the target is obtained, and the simulation and experiment verify that the corresponding algorithm can greatly improve the accuracy of target recovery.

Observed Surface Wind Field Structure of Severe Tropical Cyclones and Associated Precipitation

Using the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, this study assessed the surface wind fields from high spatial resolution Synthetic Aperture Radar (SAR) observations, the fifth generation ECMWF reanalysis for the global climate and weather (ERA5) data and the Tropical Cyclone Winds and Inflow Angle Asymmetry (TCIAA) wind model. The results showed that SAR data are sufficient to reveal the surface wind field near a TC center and can accurately describe TC intensity and size under severe TC conditions. Then, a new, improved statistical wind structure model was set up using ERA5 data alone based on the assessment. In addition, the warm sea surface (SST &gt; 26.5 °C) produced stronger TC wind fields and heavier precipitation. When the SST was higher (lower), the heavy rainfall was located on the left (right) side of the TC track and the strong positive correlation between wind speed and precipitation increased as the SST decreased.

Synthetic aperture imaging using multi-view super-resolution

The occlusion problem is a major challenge in the field of computer vision. Synthetic aperture imaging (SAI) is often used for surface reconstruction of occluded objects. However, SAI usually relies on high-speed information transmission devices. In addition, a large amount of information in the scene is lost, when handling low-resolution input images. This limitation results in unclear reconstructed regions in the synthetic aperture image and thus hinders the application of SAI in downstream tasks. We propose a multi-view super-resolution SAI method. It aims to generate high-resolution synthetic aperture images using images acquired by few of low-resolution acquisition devices. The main contributions of this paper are: (1) a multi-view super-resolution algorithm is proposed. It can generate clear synthetic aperture images in an array with a limited number of cameras. (2) By exploiting the correlation between views, the proposed algorithm can generate super-resolution synthetic aperture images with more accurate image structure and sharper image edges. (3) A feature extraction module is proposed. It can effectively extract the complementary relationship between pictures from different perspectives. The experimental results show that the proposed method can generate a reconstructed image of the occluded object surface with clear edges and accurate structure. Compared to conventional SAI, our method improves 5.7 % / 21.1 % on peak signal-to-noise ratio (PSNR)/structure similarity index measure (SSIM) and 4.4 % / 9.2 % on PSNR/SSIM respectively on two datasets compared to other state-of-the-art super-resolution methods.

Delineating Polynya Area Using Active and Passive Microwave Sensors for the Western Ross Sea Sector of Antarctica

A polynya is an area of open water or reduced concentration of sea ice surrounded by either concentrated sea ice or land ice. They are often seen as sites of intense ocean–atmosphere heat exchange and as ice production factories. Given their importance, it is crucial to quantify the accuracy of satellite-derived polynya information. Polynyas in their early evolution phase are generally narrow and occur at scales likely too fine to be detected by widely used passive microwave (PMW) radiometric sensors. We derived 40 m scale polynya information over the western Ross Sea from high-resolution Synthetic Aperture Radar (SAR) Sentinel-1 C-band data and examined discrepancies with larger-scale estimates. We utilized two automated algorithms, supervised (a rule-based approach) and unsupervised (a combination of texture analysis with k-means clustering), to accurately identify the polynya areas. We generated data for validation using Sentinel-1 data at instances where polynyas can be visually delineated. Results from PMW sensors (NSIDC and AMSR2) and SAR-based algorithms (rule-based and texture-based) are compared with manually delineated polynya areas obtained through Sentinel-1. Analysis using PMW sensors revealed that NSIDC overestimates larger polynyas and underestimates smaller polynyas compared to AMSR2. We were more accurately able to identify polynya presence and area using Sentinel-1 SAR observations, especially in clear cases and cases when PMW data miscalculates the polynya’s presence. Of our SAR-based algorithms, the rule-based approach was more accurate than the texture-based approach at identifying clear polynyas when validated against manually delineated regions. Altogether, we emphasize the need for finer spatio-temporal resolution data for polynya studies.