Radar Images Research Articles

Large‐Scale Extensional Strain in Southern Tibet From Sentinel‐1 InSAR and GNSS Data

AbstractIn this study, we utilize C‐band Sentinel‐1 radar images from 2015 to 2022, combined with interseismic horizontal GNSS velocities, to construct large‐scale, high‐resolution, 3‐D velocity and strain rate maps over a vast region of southern Tibet. We show the distribution of prevailing dilatational strain accumulation along the seven major rift zones. Using 2‐D elastic dislocations invoking a two‐fault model in a Bayesian framework, we quantified the decadal extension rates across the seven rift zones, and we suggest a total extension rate of 18.4 ± 1.7 mm/yr, consistent with geological and geodetic estimates. The resulting strain rate maps, combined with the earthquake catalog, help us identify areas with high earthquake potential. Our study enhances our understanding of the present‐day tectonics and kinematics in southern Tibet and provides important constraints for seismic hazard assessment in this region.

A Ghost and Attention Mechanism-Based Deep Learning Approach for SAR Small Target Image Detection

Sy nthetic aperture radar (SAR) images play an important role in the task of small target imaging and detection. However, due to its blurred target imaging, complex image background, and challenging feature representation, SAR detection is difficult to get high precision in a complex imaging environment. Therefore, by integrating the ghost model and the Attention-Mechanism (AM), this paper presents a new feature network model based on the improved YOLOv5s for detecting small targets in SAR images efficiently. The key is to design a Ghost_AM module by integrating the ghost bottleneck with the simple AM. This Ghost_AM module is introduced into to the YOLOv5s’ network for improving the YOLOv5s’ extraction performance for small target’s feature from a complex SAR image. Furthermore, to improve the YOLOv5s’ recognition ability for the features extracted from the Ghost_AM, a bidirectional feature pyramid network is use to replace the YOLOv5s’ feature fusion network and get a precise detection. The experiments using some SAR ship image datasets have demonstrated the presented model is better than some traditional SAR target detection algorithms and the original YOLOv5s. Moreover, the presented model’s parameters are reduced by 25.1% compared with the original YOLOv5s, which contributes to its simpler implementation.

Coseismic surface ruptures of 20 strike-slip earthquakes measured from geodetic imaging data

Observations of coseismic surface ruptures are widely used for examining faulting mechanics and kinematics, and in fault displacement hazard analysis. Here, first, we provide new remote sensing observations of 20 historic surface rupturing continental strike-slip earthquakes (6.1 ≤ Mw≤ 7.9). To measure the near-field surface deformation pattern, we have processed a range of raw radar and optical images from satellite and aerial platforms with pixel tracking techniques, and in two cases using standard interferometric synthetic aperture radar (InSAR). We provide a range of observations, including surface displacement maps that constrain at least the horizontal component of surface motion (2D), and for three events the full 3D components. Second, we provide strain invariants for each event, which includes the finite dilatational strain, strain magnitudes, the vorticity, and maximum shear strain. Third, we provide a total of 134 surface fault rupture traces that are mapped manually from the displacement maps. Finally, we provide 2648 measurements of coseismic horizontal fault-parallel surface fault slip that are measured objectively using swath profiles with a function fitting method that we have developed.

Rectifying Discrepancies Between GB-SAR Images and Terrain Model Caused by Measurement Deviations in Open-Pit Mines

Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR) is a valuable technique for monitoring deformation of landslides. GB-InSAR can construct high-resolution 2D images in a range-doppler plane. However, it is difficult for geo-engineers who are unfamiliar with radar monitoring geometry to interpret the results. Geometric mapping method has been applied to the co-registration of terrain models and radar images for the deformation zonation, but the mismatching correction with GB-InSAR and terrain model is not fully investigated by existing researches. To address this issue, this paper proposes a method exploiting an optimum linear transformation based on ground control points (GCPs). The proposed method was evaluated on simulated data and a field campaign in an open pit mine. The deformation mapping result of the method was verified by a collapse event. The method proposed in this paper has an RMSE value ranging from 0.502 to 0.720[Formula: see text]m (@700[Formula: see text]m average monitoring range) when the average density of the terrain point cloud is 0.5[Formula: see text]m. Although this method cannot accurately evaluate the antenna footprint vector deflection parameters, it can meet the needs of coarse matching calibration in relatively flat slope sub-target areas of open-pit mines for engineering applications.

A new method for enhancing signatures of ocean surface waves in nautical X-band radar images

Nautical X-band radars are used to continuously monitor large ocean surface areas with high temporal and spatial resolution. The signatures of ocean surface waves in nautical X-band radar images are typically present as straight or curved stripes with distinct directional and frequency characteristics. Curvelet transform (CT) is a multi-resolution technique with the capability of locational and directional resolving. This study presents a new method for enhancing the signatures of ocean surface waves in nautical X-band radar images. A radar image is decomposed at different scales, directions, and locations using forward CT. The surface wave signals are distributed in the curvelet coefficient (CC) of certain directions and scales. The signals of surface waves are extracted by retaining the CCs in specific directions and scales, whereas the CCs in other directions and scales are all set to zero. Wave signatures are enhanced by adding the extracted signals back to the original image. The proposed method is also feasible for enhancing signatures of surface waves in synthetic aperture radar or optical remote sensing images.

Pixel-Level Decision Fusion for Land Cover Classification Using PolSAR Data and Local Pattern Differences

Combining various viewpoints to produce coherent and cohesive results requires decision fusion. These methodologies are essential for synthesizing data from multiple sensors in remote sensing classification in order to make conclusive decisions. Using fully polarimetric Synthetic Aperture Radar (PolSAR) imagery, our study combines the benefits of both approaches for detection by extracting Pauli’s and Krogager’s decomposition components. The Local Pattern Differences (LPD) method was employed on every decomposition component for pixel-level texture feature extraction. These extracted features were utilized to train three independent classifiers. Ultimately, these findings were handled as independent decisions for each land cover type and were fused together using a decision fusion rule to produce complete and enhanced classification results. As part of our approach, after a thorough examination, the most appropriate classifiers and decision rules were exploited, as well as the mathematical foundations required for effective decision fusion. Incorporating qualitative and quantitative information into the decision fusion process ensures robust and reliable classification results. The innovation of our approach lies in the dual use of decomposition methods and the application of a simple but effective decision fusion strategy.

Spatial Feature-Based ISAR Image Registration for Space Targets

Image registration is essential for applications requiring the joint processing of inverse synthetic aperture radar (ISAR) images, such as interferometric ISAR, image enhancement, and image fusion. Traditional image registration methods, developed for optical images, often perform poorly with ISAR images due to their differing imaging mechanisms. This paper introduces a novel spatial feature-based ISAR image registration method. The method encodes spatial information by utilizing the distances and angles between dominant scatterers to construct translation and rotation-invariant feature descriptors. These feature descriptors are then used for scatterer matching, while the coordinate transformation of matched scatterers is employed to estimate image registration parameters. To mitigate the glint effects of scatterers, the random sample consensus (RANSAC) algorithm is applied for parameter estimation. By extracting global spatial information, the constructed feature curves exhibit greater stability and reliability. Additionally, using multiple dominant scatterers ensures adaptability to low signal-to-noise (SNR) ratio conditions. The effectiveness of the method is validated through both simulated and natural ISAR image sequences. Comparative performance results with traditional image registration methods, such as the SIFT, SURF and SIFT+SURF algorithms, are also included.

Investigating the performance of PBL parametrizations in WRF model for front enhanced SES simulation in istanbul megacity

Sea-effect snow (SES) is a meteorological phenomenon resulting from cold air moving over warmer waters. Accurate prediction of SES is vital for emergency management, transportation, and water resource planning. A thundersnow event in Istanbul from 17–19 February 2015 caused significant disruptions, with traffic and flights affected, highways temporarily closed, and trees falling due to heavy snowfall. This study investigates the influence of different parameterization schemes in the Weather Research and Forecasting (WRF) model on SES simulations. Six distinct PBL parameterization schemes were used in a series of WRF simulations. In addition, the following factors pivotal to SES event have also been investigated: 1000–500 hPa thickness, total and latent heat fluxes, radar and satellite analyses, temperature gradients, wind shear, inversion levels, and atmospheric stability indices. Additionally, the formation of SES during the cold front transition further contributed to these elements in the Black Sea region. The simulations displayed notably high total heat flux and latent heat flux values, particularly following the passage of the cold front. Furthermore, the northeast-southwest oriented SES cloud, distinguished by its banded structure, was successfully validated using radar and satellite imagery. However, it's worth noting that the model positioned it farther west than its actual location. This study highlights the challenges in precise prediction and analysis of such convective activities. In this thundersnow event, the local closure schemes, particularly MYNN in first place and second MYJ, demonstrated superior performance compared to non-local schemes within the parameterization options.

Improving data-driven estimation of significant wave height through preliminary training on synthetic X-band radar sea clutter imagery

Improving data-driven estimation of significant wave height through preliminary training on synthetic X-band radar sea clutter imagery

Oil pollution near the ports of Aktau, Kuryk and Ersai (the Caspian Sea): Satellite and in-situ data

To study oil pollution on the surface of the Caspian Sea in the area of the ports of Aktau, Kuryk and Ersai, the archives of radar data obtained from the synthetic aperture radars of the Sentinel-1 satellite constellation belonging to the European Space Agency for the period 2013 - 2022 were selected. For this purpose, a specialized satellite information service “See the Caspian Sea” (STCS) (http://caspian.geosmis.ru) created in 2023 jointly by Space Research Institute and Shirshov Institute of Oceanology of Russian Academy of Sciences was used. This system is specifically designed for the Caspian Sea and is created on the basis of the existing “See the Sea” (STS) service, which is part of the “IKI-Monitoring” Center for Collective Use established in Space Research Institute. For a given polygon in the region of the ports of Aktau, Kuryk and Ersai, in the STCS system, time series archives consisting of more than 3,700 radar images were examined. Each image was manually analyzed to identify dark areas of reduced radar signal that could be identified as oil contamination. A total of 15 oil slicks were discovered, up to 89 km in length and up to 8 km2 in area. A satellite image of each oil slick is presented in the article, as well as its main characteristics. In addition, as part of the project “Satellite monitoring of the water areas of the ports of Aktau and Kuryk”, sea water samples were taken in the area of the ports of Aktau and Kuryk, which showed the content of oil products in the range of 0.014-0.1 mg/dm3. Thus, the resulting concentrations turned out to be less than the maximum permissible concentration of 0.1 mg per liter.

A Methodology Based on Deep Learning for Contact Detection in Radar Images

A Methodology Based on Deep Learning for Contact Detection in Radar Images

Identifying the spatio-temporal distribution characteristics of offshore wind turbines in China from Sentinel-1 imagery using deep learning

ABSTRACT Offshore wind power is a crucial clean energy source for coastal countries and advances blue economies. Accurate spatial mapping of offshore wind turbines supports energy assessment and the sustainable development of marine resources. Offshore wind power has developed rapidly in China, but the spatiotemporal distribution characteristics of offshore wind turbines and farms remain poorly understood. Extracting nearshore small targets from Sentinel-1 synthetic aperture radar images remain challenging due to noise from the rough sea surface background and the diverse substrate types in offshore wind turbine (OWT) distributions. Here, to address these issues, we first created a dataset of China’s offshore wind turbines (COWTs) that contained typical substrate types (e.g. seawater and tidal flats). Then, a deep learning model integrating an attention mechanism and receptive fields was used to accurately detect COWTs. The well-trained model was used to detect the changes in COWTs from 2015 to 2022. Our model detected an increase in the number of offshore wind farms in China from 7 to 114 (clusters counted as an offshore wind farm), while COWTs increased from 305 to 6451 from 2015 to 2022. The Taiwan Strait exhibited the highest wind speeds, yet the Jiangsu and Guangdong regions had the most installed turbines. Over 90% of COWTs were located in areas with offshore wind energy resources less than 500 W/m3, indicating a mismatch between COWT installation and wind resource distribution. This study demonstrated temporal and geographical generalizability, which is promising for global wind power detection. Our analysis of spatiotemporal variation in COWTs facilitates informed decision-making for future field establishment and sustainable marine planning.

A Robust Translational Motion Compensation Method for Moving Target ISAR Imaging Based on Phase Difference-Lv’s Distribution and Auto-Cross-Correlation Algorithm

Translational motion compensation constitutes a pivotal and essential procedure in inverse synthetic aperture radar (ISAR) imaging. Many researchers have previously proposed their methods to address this requirement. However, conventional methods may struggle to produce satisfactory results when dealing with non-stationary moving targets or operating under conditions of low signal-to-noise ratios (SNR). Aiming at this challenge, this article proposes a parametric non-search method that contains two main stages. The radar echoes can be modeled as polynomial phase signals (PPS). In the initial stage, the energy of the received two-dimensional signal is coherently integrated into a peak point by leveraging phase difference (PD) and Lv’s distribution (LVD), from which the high-order polynomial coefficients can be obtained accurately. The estimation of the first-order coefficients is conducted during the second stage. The auto-cross-correlation function for range profiles is introduced to enhance the accuracy and robustness of estimation. Subsequently, a novel mathematical model for velocity estimation is proposed, and its least squares solution is derived. Through this model, a sub-resolution solution can be obtained without requiring interpolation. By employing all the estimated polynomial coefficients, the non-stationary motion of the target can be fully compensated, yielding the acquisition of a finely focused image. Finally, the experimental findings validate the superiority and robustness of the proposed method in comparison to state-of-the-art approaches.

Robust target recognition in synthetic aperture radar images via correlation analysis and joint decision of multi-level bidimensional intrinsic mode functions by bidimensional variational mode decomposition

Robust target recognition in synthetic aperture radar images via correlation analysis and joint decision of multi-level bidimensional intrinsic mode functions by bidimensional variational mode decomposition

Drone Swarm Classification from ISAR Imaging

In today's technology, the use of drones has become very popular for they can be easily purchased over the Internet and can be easily developed. With drones that have wide usage areas, swarm structures have become popular. However, this has brought about some problems. The issue of drone detection has emerged in order to prevent the uncontrolled use of drone swarms in the airspace. Drone swarm detection is important to prevent dangerous accidents or criminal acts. In this study, a new classification algorithm is proposed with deep learning using inverse synthetic aperture radar (ISAR) images of drone swarms based on various formation swarm types. ISAR images are created using ANSYS simulation. Additionally, high frequency structural simulator (HFSS) - shooting bouncing ray (SBR+) solver is used for high-speed computation. Radar and simulation parameters to obtain ISAR images are discussed. Especially, down-range and cross-range resolution parameters are taken into account to achieve high resolution. ISAR images are classified using deep learning methods in terms of formation. Formation types include Line, Square, Cross, and Triangle. The convolutional neural network (CNN) model is used to solve classification problems. The model consists of train, validation, and test steps. Classification performance results are presented with high accuracy. The developed method can be used for anti-drone technologies.

Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.

Refined 70-cm earth-based lunar radar maps and a new interpretation of the Cruger-Sirsalis cryptomare

We present a new processing approach for Earth-based, 70-cm wavelength, dual-polarization radar data collected using the Arecibo and Green Bank Telescopes. Earth-based data represent the only view of the Moon at this wavelength. This methodology greatly improves the synthetic aperture focusing of the images to their best possible spatial resolution, with a concurrent improvement in signal-to-noise ratio and multi-look summing. Image coverage is also expanded to the full illuminated beam area. Registration to a visible image basemap reduces RMS geographic placement errors to less than ∼2 km. Images collected after the first Planetary Data System archive delivery are also included. Analysis of the circular polarization ratio (CPR) suggests an uncertainty of ∼20 %, based on the likely ∼1 dB error in estimating the background noise in a radar look. We use the new 70-cm maps with topography and 12.6-cm radar images to revisit an initial study of cryptomare units in the Cruger-Sirsalis region, and find that mare contamination of the Orientale ejecta must be well-mixed with highland material, as opposed to the earlier hypothesis of a highland-dominated blanket over mare-rich regolith. This result shows that the two radar wavelengths provide a more complete view of the cryptomare regolith cross section, and that radar signatures can delineate ilmenite contamination of highlands areas where multi-spectral methods are limited by other factors.

Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI

Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI

Ship target detection in SAR images based on SimAM attention YOLOv8

AbstractDeep learning has been widely applied in ship detection in synthetic aperture radar (SAR) imagery due to their powerful feature representation capabilities. However, YOLOv8 models treat all regions of the image equally during convolutional feature processing, resulting in less‐than‐ideal outcomes. To address this limitation, this study proposes a simple, parameter‐free attention module (SimAM) attention‐based YOLOv8 algorithm for ship detection in SAR images. The proposed algorithm first passes through a backbone network, which incorporates SimAM attention modules. The SimAM attention mechanism successfully allocates the convolutional neural network's 3D weights effectively using an energy function method, without introducing additional parameters. This mechanism enables the network to automatically emphasize key features in the image, enhancing its ability to represent target areas and suppress background interference. Subsequently, deep features are upsampled and fused with relatively shallow features to extract features at three different scales and achieve target detection, ultimately outputting classification and positional information of the targets. The effectiveness of the model on the SAR‐ship‐dataset is experimentally validated achieving an mAP50 value of 97.72% and an mAP50‐95 value of 68.99%, confirming the superiority of the proposed model.

Forest Canopy Height Estimation Combining Dual-Polarization PolSAR and Spaceborne LiDAR Data

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available.