Synthetic Aperture Radar Images Research Articles

A systematic correlation analysis for regression model selection: Application to bridge response prediction using contact and remote sensor systems

Correlation analysis is a crucial step prior to any regression modeling for data prediction, as it can unveil the relationship between predictors and responses, particularly in terms of linearity and nonlinearity. This step is often mandatory for selecting the most suitable regression model. The major challenge is that linear correlation measures are only appropriate for linear cases and there are limited nonlinear measures for nonlinearity assessment. Apart from these challenges, the demanding issue relates to the influence of unknown and unmeasured predictor data in regression model selection thereby yielding unrealistic and inaccurate outputs from both linear and nonlinear correlation measures. To tackle these challenges, this paper proposes a hierarchical correlation analysis method that initially indicates the influence of unknown predictors and then assists in selecting the best regressor for modeling and forecasting. The proposed method utilizes a linear measure known as canonical correlation analysis and a nonlinear measure known as the maximal information criterion. Based on the correlation values derived from these measures, it is possible to classify them into three levels of correlation: low, moderate, and high. In the first and second scenarios, it is inferred that the response data is linearly or nonlinearly influenced by the measured predictors, respectively. In the third scenario, three conditions are derived from the correlation levels, suggesting that the response data is not influenced by the measured predictors. To validate the proposed hierarchical correlation analysis method's applicability and effectiveness, it is tested with predictor and response data related to real-world long-span bridges. The only measured predictor is air temperature, while the response data comprises limited bridge displacements obtained from synthetic aperture radar images, in line with remote sensing technology. The results indicate that the proposed method is highly effective and applicable for selecting the best regression model for prediction.

Multi‐objective terminal trajectory optimization based on hybrid genetic algorithm pseudospectral method

AbstractDuring terminal guidance, the attack platform is provided with a high‐resolution image of the target area through the application of synthetic aperture radar. Additionally, the stealth trajectory with low observability can significantly impact mission success. This paper considers both the performance of missile‐borne synthetic aperture radar imaging and stealth performance as influencing factors for terminal trajectory optimization, which is modelled as a constrained multi‐objective optimization problem. The application of the pseudospectral method in the solution of optimal control problems has led to the proposal of the hybrid genetic algorithm pseudospectral optimization framework. The problem is decomposed into several single‐objective optimal control problems, which can generate a specific initial population for the genetic algorithm to obtain a set of Pareto‐optimal solutions. Finally, the numerical simulations demonstrate the effectiveness of the proposed optimization approach compared with the benchmark scheme.

Bayesian Data Fusion for Enhanced Monitoring of Bridge Displacements Using Satellite InSAR and Topographic Techniques

This paper investigates the effectiveness of satellite Interferometric Synthetic Aperture Radar (InSAR) technology to remotely monitor the structural health of road bridges and detect possible unexpected behaviours. The study is based on an Italian multi-span prestressed concrete bridge and a dataset of eight years of X-band SAR images collected by the COSMO-SkyMed satellite constellation. The method implemented for this study is as follows. First, SAR images are analysed using the Multi-Temporal InSAR technique to extract displacement time series of reflective targets on the bridge decks. Then, the extracted displacements are interpreted to validate their consistency with the bridge's expected responses to environmental loads, such as air temperature and river water flow variations, and detect possible unexpected behaviours. Eventually, the correlation with the air temperature is investigated to identify different bridge spans based only on monitoring data. The findings from this study advance the knowledge on using satellite InSAR-based monitoring for early detection of anomalous bridge behaviours on a large scale, providing valuable insights for the maintenance and safety of critical infrastructure.

A GAN-based fast focusing method for circular SAR images

Circular Synthetic Aperture Radar(CSAR) imaging is vulnerable to perturbations in the atmosphere and various other elements that can lead to position offset errors in the antenna's phase center as well as induce motion errors. Traditional phase compensation methods that operate in the time domain, such as Auto-regressive Back-projection (ARBP), typically require computation on a direction-by-direction basis, which can result in the considerable expenditure of time and memory resources. To address these challenges, thispaperintroduces a novel approach for focusing on CSAR images. This method leverages the training of a Generative Adversarial Network (GAN) to directly achieve focus on CSAR sub-aperture images. Additionally, to counteract the network's tendency towards low-frequency preferences, the Auto-focus Frequency Loss (AFFL) is introduced. Moreover, to enhance the accuracy of focus position extraction, the Focus Position Feature Attention (FPFA) is proposed. These innovations, along with a new fusion strategy for the sub-aperture images post-focusing, have been experimentally validated, demonstrating significant improvements in the efficiency and accuracy of CSAR image focusing.

Automatic detection and tracking polar lows from synthetic aperture radar and radiometer observations

ABSTRACT Polar lows are small, high-latitude, intense maritime cyclones and frequently have severe impacts on the ocean such as strong winds, high waves and heavy rainfall. They are difficult to observe and forecast due to their short lifetime (<48 hours), small horizontal scales (200 ~ 1000 km), and the sparse synoptic observing network that exists in the subarctic and Arctic oceans. Previous studies have identified and monitored polar lows by visual analysis of visible and thermal infrared imagery from satellites. However, this manual inspection method is subjective, time-consuming and inevitably involves errors in polar low detections. In this study, we present an automatic objective procedure which we demonstrate by detecting polar lows using spaceborne active synthetic aperture radar (SAR) and passive microwave radiometer observations. Based on the marker-controlled watershed segmentation method and the morphological image thinning algorithm, the centre locations of polar lows are determined using RADARSAT-2 and Sentinel-1A high-resolution SAR images and total atmospheric water vapour content fields from radiometers (AMSR2, SSM/I, GMI, and WindSat). Furthermore, the trajectories of polar lows are constructed, using detected centres from multi-temporal SAR and radiometer observations. Polar low detections are confirmed by high surface wind speeds from SAR, scatterometer, and radiometer data, the presence of cloud vortex signatures visible in MODIS, AVHRR, and VIIRS thermal infrared imagery, as well as the difference between the sea surface temperature and the air temperature at 500 hPa. These results show that the proposed methods have potential to automatically detect and track polar lows from multisensor data. We also estimate the characteristic parameters of detected polar lows. The diameters, translation speeds, and distances travelled are 189 km and 225 km, 8 m/s and 4.9 m/s, and 318 km and 263 km, respectively.

Surface deformation analysis due to the Poso earthquake on May 29, 2017 using the DInSAR method

Abstract Deformation can be a significant parameter in determining the presence and impact of an earthquake. The amount of deformation can be obtained from SAR image data using the InSAR and DinSAR methods. The DinSAR (Differential Interferometric Synthetic Aperture Radar) method is a well-developed method for observing subsidence and uplift with high accuracy in centimeters (cm). In this study, SAR imagery is used to detect surface deformation caused by the earthquake was happened on May 29, 2017, at 21:35:23 WIB in the Poso Regency, Central Sulawesi. The data used is Sentinel-1 satellite imagery data in single-look complex (SLC) format consisting of Master image recorded on May 19, 2017 (10 days before the main earthquake occurred) and Slave image recorded on May 31, 2017 (2 days after the main earthquake occurred). The interferogram formed during the Poso earthquake is around the main earthquake directions from the Southwest, South, to the Southeast. The amount of deformation generated in the deformation phase (wrapped phase) is in the form of negative and positive phase values. Negative phase values indicate areas experiencing deformation in the form of subsidence. The positive phase values indicate areas experiencing uplift. The results of unwrapping the SAR imagery obtained a maximum uplift deformation value of 0.0663 meters, spread over the areas of North Pesisir Poso District, Lore Peore District, Central Lore District, South Coastal Poso District, and East Lore District. The maximum subsidence deformation values (0.076 meters) that are quite large are spread in the North Coastal Poso District, Poso Coastal District, Lage District, and Lore Tengah District.

Comparison between NDVI and InSAR data coherence in deformation analysis caused by the 2022 Pasaman earthquake

Abstract One of the destructive earthquakes occurred in Pasaman Barat on February 25, 2022, with a magnitude of 6.1 Mw that caused 27 deaths, 19,221 people evacuated, and an estimated 6,627 houses destroyed. Currently, InSAR is one of the fastest methods to analyze the deformation impacted by an earthquake from processing Sentinel-1 Single Look Complex images. Analyzing the deformation of the 2022 Pasaman Earthquake using InSAR could be challenging because the area consists of high vegetation. It can cause decorrelation due to time difference which increases the possibility of the low coherence value of this data. Excluding the low-coherence result is essential for a better interpretation. This study will analyze the correlation between NDVI and interferogram coherence and eliminate low-coherence data for the displacement deformation map. The NDVI value was calculated using band 4 (Red) and band 5 (NIR) from Landsat 8 images. The InSAR was processed using SNAP, starting from interferogram formation until the conversion phase into displacements, and the map visualization was designed using QGIS. This application of the InSAR method (ascending mode) from Sentinel-1 SAR images can show the deformation, but the coherence level is relatively low. Deformations around the earthquake are shown as uplift and subsidence near the epicenter of the 2022 Pasaman Earthquakes. The high value of vegetation density in the study area causes low coherence between images, so the fringes that identify deformation due to the 2022 Pasaman Earthquake were not seen significantly for the entire study area.

Spaceborne Synthetic Aperture Radar Aerial Moving Target Detection Based on Two-Dimensional Velocity Search

Synthetic aperture radar (SAR) can detect moving targets on the ground/sea, and high-resolution imaging on the ground/sea has critical applications in both military and civilian fields. This paper attempts to use a spaceborne SAR system to detect and image moving targets in the air for the first time. Due to the high velocity of aerial targets, they usually appear as two-dimensional range and azimuth direction defocus in SAR images, and clutter will also have a profound impact on target detection. To solve the above problems, a method of detecting and focusing on a spaceborne SAR target based on a two-dimensional velocity search is proposed by combining the BP algorithm. According to the current environment of the aerial target and the number of system channels, the clutter suppression methods are set and combined with two-dimensional velocity search with different precision, the Shannon entropy under different search velocity groups is used to obtain the search velocity group closest to the actual velocity and realize the integrated processing of moving target detection–focused imaging parameter estimation. Combined with simulation data, the effectiveness of the proposed method is verified.

Calibration of SAR Polarimetric Images by Covariance Matching Estimation Technique with Initial Search

To date, various methods have been proposed for calibrating polarimetric synthetic aperture radar (SAR) using distributed targets. Some studies have utilized the covariance matching estimation technique (Comet) for SAR data calibration. However, practical applications have revealed issues stemming from ill-conditioned problems due to the analytical solution in the iterative process. To tackle this challenge, an improved method called Comet IS is introduced. Firstly, we introduce an outlier detection mechanism which is based on the Quegan algorithm’s results. Next, we incorporate an initial search approach which is based on the interior point method for recalibration. With the outlier detection mechanism in place, the algorithm can recalibrate iteratively until the results are correct. Simulation experiments reveal that the improved algorithm outperforms the original one. Furthermore, we compare the improved method with Quegan and Ainsworth algorithms, demonstrating its superior performance in calibration. Furthermore, we validate our method’s advancement using real data and corner reflectors. Compared with the other two algorithms, the improved performance in crosstalk isolation and channel imbalance is significant. This research provides a more reliable and effective approach for polarimetric SAR calibration, which is significant for enhancing SAR imaging quality.

Recognition of small water bodies under complex terrain based on SAR and optical image fusion algorithm

Understanding the spatial and temporal distribution of small water bodies is essential for managing water resources, crafting conservation policies, and preserving watershed ecosystems and biodiversity. However, existing studies often rely on a single remote sensing data source (optical or microwave), focusing on large-scale, flat areas and lacking comprehensive monitoring of small water bodies in complex terrain. Therefore, considering the complementary advantages of multisource remote sensing (multispectral and SAR), this paper proposes a multispectral and SAR fusion algorithm, named Multispectral and SAR Fusion algorithm (MASF), to better capture the remote sensing characteristics of small water bodies in complex areas. Based on this, a dataset containing spectral, texture, and geometric features is constructed, and multi-scale segmentation and random forest algorithms are applied for identification of small water bodies in complex terrain. The results demonstrate that the proposed fusion algorithm MASF exhibits minimal spectral distortion (SAM < 3.5, ERGAS <21, RMSE <0.01) and robust spatial feature enhancement (PSNR >40, SSIM >0.999, CC > 0.99). The Overall Accuracy (OA) and Kappa coefficients for both experimental areas surpassed 0.9. For rivers and reservoirs, both Producer's Accuracy (PA) and User's Accuracy (UA) exceeded 0.9. The UA for agricultural ponds exceeded 0.8. Comparative analysis with three other types of water-related data products shows that the freshwater identification results in this study have certain advantages in local small water bodies. Our research holds significant implications for the utilization of water resources in mountainous areas, prevention and control of floods and floods, as well as the development of aquaculture industry.

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

A SAR Ship Detection Method Based on Adversarial Training.

SAR (synthetic aperture radar) ship detection is a hot topic due to the breadth of its application. However, limited by the volume of the SAR image, the generalization ability of the detector is low, which makes it difficult to adapt to new scenes. Although many data augmentation methods-for example, clipping, pasting, and mixing-are used, the accuracy is improved little. In order to solve this problem, the adversarial training is used for data generation in this paper. Perturbation is added to the SAR image to generate new samples for training, and it can make the detector learn more abundant features and promote the robustness of the detector. By separating batch normalization between clean samples and disturbed images, the performance degradation on clean samples is avoided. By simultaneously perturbing and selecting large losses of classification and location, it can keep the detector adaptable to more confrontational samples. The optimization efficiency and results are improved through K-step average perturbation and one-step gradient descent. The experiments on different detectors show that the proposed method achieves 8%, 10%, and 17% AP (Average Precision) improvement on the SSDD, SAR-Ship-Dataset, and AIR-SARShip, compared to the traditional data augmentation methods.

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.

Using SAR Data for Monitoring of Agricultural Crops in the South of the Russian Far East

The use of SAR data to monitoring agricultural crops is a promising area of research designed to complement existing methods and technologies based on the analysis of multispectral images. The main advantages of vegetation indices calculated from SAR data include their sensitivity to the polarimetric properties of the backscatter intensity, its scattering characteristics, and independence from cloud cover. This is especially important for the territory of the south of the Russian Far East, whose monsoon climate provides humid and cloudy weather during the period when crops gain maximum biomass. For arable lands in the Khabarovsk Territory and the Amur Region, a total of 64 Sentinel-1 SAR images were obtained from May to October 2021. For each date, the values of the DpRVI, RVI, VH/VV indices were calculated and time series were constructed for the entire observation period for individual fields (342 fields in total). NDVI time series were constructed from Sentinel-2 multispectral images using a cloud mask. The characteristics of time series extremes were calculated for different types of arable land: soybeans, oats, and fallows. It was shown that for each crop the seasonal curves DpRVI, RVI, VH/VV had a characteristic appearance. It was found that the DpRVI demonstrated the highest stability – the coefficients of variation of the seasonal variation of DpRVI were significantly lower than those for RVI and VH/VV. It was also revealed that the similarity between the curves of these indices remained for regions quite distant from each other - the Khabarovsk Territory and the Amur Region. The main characteristics of the seasonal variation of time series of radar indices were calculated in comparison with NDVI - the magnitude of the maximum, the date of the maximum and the values of the coefficient of variation for these indicators. It was found, firstly, that the values of these indicators in different regions are similar to each other; secondly, the variability of the maximum and the day of the maximum for DpRVI is lower than for RVI and VH/VV; thirdly, the variability of the maximum and the day of the maximum for DpRVI is comparable to NDVI. Thus, time series of radar indices DpRVI, RVI, VH/VV for the main types of agricultural lands in the south of the Far East have distinctive features and can be used in classification problems, yield modeling and crop rotation control.

Relationships between internal solitary wave surface features in optical and SAR satellite images: Insights from remote sensing and laboratory

Internal solitary waves (ISWs) induce convergence and divergence of sea surface currents, manifesting as alternating bright and dark stripes in optical and synthetic aperture radar (SAR) remote sensing images. Although the relationship between ISW surface features in SAR images and ISW-induced surface currents has been extensively explored, it has not been clearly quantified in optical images. This study contrasts the surface features of the same ISWs using optical and SAR images with short time intervals and statistically analyses the 450 ISW stripe widths in the northern South China Sea and the Andaman Sea. The results demonstrate that the ISW surface features in optical and SAR images differ, with the ISW stripe widths of SAR images being 0.83 times those of optical images. Laboratory experiments are conducted to simulate the ISW surface features of optical and SAR images, which exhibit a consistent quantitative relationship with remote sensing results. Further experiments reveal that the ISW-induced free surface displacement is the cause of the differences in the ISW surface features between optical and SAR images. This research provides support for broader quantitative applications of optical remote sensing images.

A multi-scale context-aware and batch-independent lightweight network for green tide extraction from SAR images

ABSTRACT The outbreaks of green tide have caused severe harm to the marine environment and human society. Synthetic Aperture Radar (SAR) plays an important role in green tide monitoring by virtue of its high resolution and cloud-free nature. The existing green tide extraction methods still face challenges in identifying multi-scale green tide patches due to noise interference, uneven greyscale and blurred boundaries in SAR images. Meanwhile, the practical application of deep learning methods with high precision is limited due to the complexity of the model and the large amount of computation. Therefore, we propose a multi-scale context-aware and batch-independent lightweight green tide extraction network called MBL-Net. A novel lightweight heterogeneous backbone is designed to extract multi-scale discriminative features and improve segmentation efficiency by using multi-scale selection kernel (MSK) modules and lightweight stages. Meanwhile, Triplet attention module is introduced to improve the internal consistency of the green tide region and suppress the effect of speckle noise. Then, the mixed pooling-based channel prior module (MCPM) is used to expand the receptive field of the network and extract the fine green tide structure by fusing multi-scale features. In addition, Filter Response Normalisation (FRN) is innovatively applied for feature normalization in the decoding stage, eliminating batch dependency. In order to verify the effectiveness of the proposed method, a dataset is built using the Sentinel-1 images of the Yellow Sea, China, from 2019 to 2021. The experimental results show that the proposed method achieves an overall accuracy of 98.59% with 0.970 G FLOPs and 3.525 M parameters, which ensures high precision and improves green tide detection efficiency. Compared with several representative networks, this method can capture more details of green tide with fewer parameters and faster calculation speed.

Application of adaptive fusion to attributed scattering centre-based reconstruction for SAR target recognition

ABSTRACT Synthetic aperture radar (SAR) measures backscattering characteristics of man-made targets and has the advantage of not being affected by weather and time. For target recognition in SAR images, the target reconstruction results based on attribute scattering centres are introduced for decision fusion. In comparison with original images, the reconstructed targets better relieve corruptions caused by noises, clutters, etc. A deep learning model, i.e. ResNet, is adopted as a basic classifier to classify both original and reconstructed images. According to the energy relationship between the reconstruction target and residual, the noise level of the original SAR image is defined. Then, the adaptive weights of original and reconstructed images are determined and a weighted decision fusion process is conducted to combine decisions from both images to confirm the target label. The proposed method is tested based on MSTAR dataset, and experimental results show its effectiveness.

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

Adaptive Bayesian speckle reduction in the 2D DOST domain using 2D MC-GARCH-M model: Preserving image edges and textural information

Image denoising and image quality enhancement are major issues in image processing. Additive and multiplicative noise removal is one of the main image enhancement approaches. In this paper, we propose a novel 2D Merged Complex Generalized Autoregressive Conditional Heteroscedasticity Mixture (MC-GARCH-M) model for 2D complex stochastic processes. This model effectively captures non-Gaussian statistics and all three types of dependencies within the processes (RRD, IID, and RID) using the proposed linear mapping transformation T. Our statistical analysis shows that the 2D MC-GARCH-M model provides the best fit to the non-Gaussian statistics of the mapped matrix of 2D DOST coefficients compared to Gaussian, Generalized Gaussian, and Stable distributions. We confirm the presence of heteroscedasticity in the mapped matrix using the Engle hypothesis test, verifying ARCH/GARCH effects. The 2D MC-GARCH-M model, with its location-dependent conditional variances, provides a flexible approach for noise removal and modeling complex stochastic processes. Experimental results on artificially speckled aerial and actual SAR images demonstrate that our method outperforms state-of-the-art approaches in terms of speckle removal and preserving the edges and textural information of the image. Key innovations include: a new statistical analysis using linear mapping T showing non-Gaussian heavy-tailed distributions; an adaptive Bayesian estimator, MCMAP, based on the heteroscedastic model for speckle noise removal; and the method's applicability to images with varying mutual information (MI) between real and imaginary parts of 2D DOST coefficients. Our method is adaptive and robust to initial parameter settings, effectively utilizing magnitude and phase information, providing an optimal closed-form solution, reducing memory and computational requirements, and demonstrating robustness to speckle power levels. While requiring more computational resources compared to thresholding methods, our approach is less demanding than other Bayesian algorithms, making it suitable for offline processing.