Synthetic Aperture Radar Images Research Articles

Quality Improvement Synthetic Aperture Radar (SAR) Images Using Compressive Sensing (CS) With Moore-Penrose Inverse (MPI) and Prior From Spatial Variant Apodization (SVA).

When the locations of non-zero samples are known, the Moore-Penrose inverse (MPI) can be used for the data recovery of compressive sensing (CS). First, the prior from the locations is used to shrink the measurement matrix in CS. Then the data can be recovered by using MPI with such shrinking matrix. We can also prove that the results of data recovery from the original CS and our MPI-based method are the same mathematically. Based on such finding, a novel sidelobe-reduction method for synthetic aperture radar (SAR) and Polarimetric SAR (POLSAR) images is studied. The aim of sidelobe reduction is to recover the samples within the mainlobes and suppress the ones within the sidelobes. In our study, prior from spatial variant apodization (SVA) is used to determine the locations of the mainlobes and the sidelobes, respectively. With CS, the mainlobe area can be well recovered. Samples within the sidelobe areas are also recovered using background fusion. Our method is suitable for acquired data with large sizes. The performance of the proposed algorithm is evaluated with acquired space-borne SAR and air-borne POLSAR data. In our experiments, we use the [Formula: see text] space-borne SAR data with the size of 10000 (samples) × 10000 (samples) and [Formula: see text] POLSAR data with the size of 10000 (samples) × 26000 (samples) for sidelobe suppression. Furthermore, We also verified that, our method does not affect the polarization signatures. The effectiveness for the sidelobe suppression is qualitatively examined, and results were satisfactory.

Research on SAR image quality evaluation method based on improved harris hawk optimization algorithm and XGBoost

Research on SAR image quality evaluation method based on improved harris hawk optimization algorithm and XGBoost

HCA-RFLA: A SAR Remote Sensing Ship Detection Based on Hierarchical Collaborative Attention Method and Gaussian Receptive Field-Driven Label Assignment Strategy

Ensuring safety at sea has become a primary focus of marine monitoring, driving the increasing adoption of ship detection technology in the maritime industry. Detecting small ship targets in SAR images presents challenges, as they occupy only a small portion of the image and exhibit subtle features, reducing detection efficiency. To address these challenges, we propose the HCA-RFLA algorithm for ship detection in SAR remote sensing. To better capture small targets, we design a hierarchical collaborative attention (HCA) mechanism that enhances feature representation by integrating multi-level features with contextual information. Additionally, due to the scarcity of positive samples for small targets under IoU and center sampling strategies, we propose a label assignment strategy based on Gaussian receptive fields, known as RFLA. RFLA assigns positive samples to small targets based on the Gaussian distribution between feature points and ground truth, increasing the model’s sensitivity to small samples. The HCA-RFLA was experimentally validated using the SSDD, HRSID, and SSD datasets. Compared to other state-of-the-art methods, HCA-RFLA improves detection accuracy by 6.2%, 4.4%, and 3.6%, respectively. These results demonstrate that HCA-RFLA outperforms existing algorithms in SAR remote sensing ship detection.

Analysis of Scattering Mechanisms in SAR Image Simulations of Japanese Wooden Buildings Damaged by Earthquake

The difficulty in identifying collapsed houses and damaged structures in synthetic aperture radar (SAR) images after natural disasters represents a significant challenge in the monitoring of urban structural deformation using SAR. SAR image simulation was conducted on a three-dimensional model of a typical wooden building in Japan to analyze the scattering mechanism of the structure in collapsed and uncollapsed states. Based on the physical properties of the buildings, a correlation was established between the simulated SAR image feature signals and the geometric structures of the buildings. The findings indicate that SAR scattering is more uniform for uncollapsed structures, which is predominantly influenced by their geometry. At low incidence angles, single reflections were the predominant phenomenon, whereas at high incidence angles, multiple reflections became more prevalent. The uncollapsed building’s facade formed a dihedral angle, exhibiting bright lines in the SAR image. Multiple reflections occurred at the edges of the building and floor junctions. These findings follow the theoretical predictions. In the case of the collapsed buildings, multiple reflections occurred with greater frequency, and irregular scattering was observed. Notwithstanding the augmented scattering pathways, some walls nevertheless manifested single reflections. The collapsed structures demonstrated a reduced sensitivity to alterations in the angle of incidence.

Integration of Optical and Synthetic Aperture Radar Data with Different Synthetic Aperture Radar Image Processing Techniques and Development Stages to Improve Soybean Yield Prediction

Predicting crop yield throughout its development cycle is crucial for planning storage, processing, and distribution. Optical remote sensing has been used for yield prediction but has limitations, such as cloud interference and only capturing canopy-level data. Synthetic Aperture Radar (SAR) complements optical data by capturing information even in cloudy conditions and providing additional plant insights. This study aimed to explore the correlation of SAR variables with soybean yield at different crop stages, testing if SAR data enhances predictions compared to optical data alone. Data from three growing seasons were collected from an area of 106 hectares, using eight SAR variables (Alpha, Entropy, DPSVI, RFDI, Pol, RVI, VH, and VV) and four speckle noise filters. The Random Forest algorithm was applied, combining SAR variables with the EVI optical index. Although none of the SAR variables showed strong correlations with yield (r < |0.35|), predictions improved when SAR data were included. The best performance was achieved using DPSVI with the Boxcar filter, combined with EVI during the maturation stage (with EVI:RMSE = 0.43, 0.49, and 0.60, respectively, for each season; while EVI + DPSVI:RMSE = 0.39, 0.49, and 0.42). Despite improving predictions, the computational demands of SAR processing must be considered, especially when optical data are limited due to cloud cover.

High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction

High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have a theoretical geolocation accuracy better than 1 m, but this may be unrealizable with less accurate external data, such as atmospheric parameters and ground elevations. To investigate the actual SAR geolocation accuracy in common applications, we analyze the properties of different geolocation errors, propose a geolocation procedure, and conduct experiments on TerraSAR-X images and a pair of Tianhui-2 images. The results show that based on GNSS elevations, the geolocation accuracy is better than 1 m for TerraSAR-X and 2 m/4 m for the Tianhui-2 reference/secondary satellites. Based on the WorldDEM and the SRTM, additional geolocation errors of 2 m and 4 m are introduced, respectively. By comparing the effectiveness of different tropospheric correction methods, we find that the GACOS mapping method has advantages in terms of resolution and computational efficiency. We conclude that tropospheric errors and ground elevation errors are the primary factors influencing geolocation accuracy, and the key to improving accuracy is to use higher-accuracy DEMs. Additionally, we propose and validate a geolocation model for the Tianhui-2 secondary satellite.

Combining Sentinel-2 and diverse environmental data largely improved aboveground biomass estimation in China's boreal forests.

Accurately mapping aboveground biomass (AGB) in China's boreal forests is crucial for assessing global carbon stock and formulating forest management strategies but remains challenging as the environmental heterogeneity complicates AGB estimation. Here, we investigated the relative gains of integrating Sentinel-2 and environmental data, as well as synthetic aperture radar (SAR) images to map AGB in China's boreal forests. We used two machine learning algorithms, random forest and gradient boosting regression (GBR), and four dataset combinations to develop the AGB models, then evaluated the AGB map by carrying on uncertainty analysis and comparing it with existing AGB products. Results showed that the GBR model based on Sentinel-2 and environmental data presented the best AGB estimation capability (R2: 0.75, RMSE: 23.60 Mg/ha), while further adding SAR images had negative effects on the model improvement. The Tasseled Cap Distance, short-wave infrared from Sentinel-2, Black dragon fire disturbance, Elevation, and Geographic locations were found to be significant contributors to AGB prediction. Our AGB estimates exhibited moderate to low uncertainty and outperformed other existing AGB maps in China's boreal forests based on independent validation assessment. The AGB distribution presented a noticeable south-north gradient difference, ranging from 3.23 to 346.37 Mg/ha. This study provides new insight into AGB estimation through the integration of Sentinel-2 imagery and multiple environmental data and offers a basis for sustainable management in China's boreal forests.

Long-Term Ground Deformation Monitoring and Quantitative Interpretation in Shanghai Using Multi-Platform TS-InSAR, PCA, and K-Means Clustering

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals’ lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas.

Enhanced TomoSAR imaging method with complex least squares for 3D building reconstruction

ABSTRACT Synthetic aperture radar tomography (TomoSAR) has emerged as an advanced technology for the rapid acquisition of three-dimensional information, with model solutions dependent on a specific complex least squares adjustment criterion. We design two TomoSAR imaging algorithms based on two adjustment criterions minimizing the square sum of the real and imaginary components of observation vector residual, and minimizing the square sum of the L2-norm of observation vector residual. Additionally, we propose a strong scatterer recognition method that combines amplitude thresholding and density-based spatial clustering of applications with noise to alleviate the low efficiency and excessive noise in real images. The simulated and practical experiments are conducted to evaluate the performance of algorithms. Results suggested that two algorithms perform similarly in parameter estimation accuracy and noise immunity for buildings with simple structures and single scatterer. However, for buildings with multiple scatterers, the algorithm derived by minimizing the square sum of the L2-norm from the observation vector residual exhibits more robustly than the other algorithm, accurately distinguishing multiple scatterers within pixels and reconstructing 3D scenes mirroring the actual ones. The results verify the effectiveness of the proposed strong scatterer recognition method and the importance of the adjustment criterion in TomoSAR imaging.

Semantic information guided diffusion posterior sampling for remote sensing image fusion

The task of image fusion for optical images and SAR images is to integrate valuable information from source images. Recently, owing to powerful generation, diffusion models, e.g., diffusion denoising probabilistic model and score-based diffusion model, are flourished in image processing, and there are some effective attempts in image fusion by scholars’ progressive explorations. However, the diffusion models for image fusion suffer from inevitable SAR speckle that seriously shelters from effective information in the same location of optical image. Besides, these methods integrate pixel-level features without information for high-level tasks, e.g., target detection and image classification, which leads fused images are insufficient and their application accuracies are low, for high-level tasks. To tackle these hurdles, we propose the semantic information guided diffusion posterior sampling for image fusion. Firstly, we employ the SAR-BM3D as preprocessing to despeckle. Then, the sampling model is established with fidelity, regularization and semantic information guidance term. The first two terms are obtained by the variational diffusion method via variational inference and first-order stochastic optimization. The last term is served by cross entropy loss between annotation and classification result from FLCNet we design. Finally, the experiments validate the feasibility and superiority of the proposed method on WHU-OPT-SAR dataset and DDHRNet dataset.

The Latest Development of Synthetic Aperture Radar: An Overview

Abstract. Synthetic Aperture Radar (SAR) technology enables high-resolution imaging regardless of weather conditions, offering immense potential for target detection and recognition. This paper reviews the latest advancements in SAR technology, highlighting key developments and applications. The limitations of traditional SAR imaging techniques, particularly the trade-off between resolution and swath width, have been addressed by High Resolution Wide Swath (HRWS) SAR, enabling simultaneous acquisition of high-resolution and wide-swath data. Multi-polarimetric SAR provides richer scattering information for improved target recognition accuracy, while deep learning algorithms enhance image quality and recognition performance. Emerging technologies like 3D-ISAR and holographic imaging offer novel approaches to target recognition and tracking. We discuss the development history, performance advantages, and limitations of these technologies, providing a comprehensive overview of their practical applications. We identify current challenges and future trends in SAR target imaging and recognition, including computational efficiency, adversarial attack defense, data acquisition and processing, and feature extraction and classification. Potential research directions, such as integrated multi-mode SAR systems, multi-source data fusion, and adversarial attack defense strategies, are outlined to fully harness the potential of SAR technology in target imaging and recognition.

HDRSA-Net: Hybrid dynamic residual self-attention network for SAR-assisted optical image cloud and shadow removal

Clouds and shadows often contaminate optical remote sensing images, resulting in missing information. Consequently, continuous spatiotemporal monitoring of the Earth’s surface requires the efficient removal of clouds and shadows. Unlike optical satellites, synthetic aperture radar (SAR) has active imaging capabilities in all weather conditions, supplying valuable supplementary information for reconstructing missing regions. Nevertheless, the reconstruction of high-fidelity cloud-free images based on SAR-optical data fusion remains challenging due to differences in imaging mechanisms and the considerable contamination from speckle noise inherent in SAR imagery. To solve the aforementioned challenges, this paper presents a novel hybrid dynamic residual self-attention network (HDRSA-Net), aiming to fully exploit the potential of SAR images in reconstructing missing regions. The proposed HDRSA-Net comprises multiple dynamic interaction residual (DIR) groups organized into an end-to-end trainable deep hierarchical stacked architecture. Specifically, the omni-dimensional dynamic local exploration (ODDLE) module and the sparse global context aggregation (SGCA) module are used to form a local–global feature adaptive extraction and implicit enhancement. A multi-task cooperative optimization loss function is designed to ensure that the results exhibit high spectral fidelity and coherent spatial structures. Additionally, this paper releases a large dataset that can comprehensively evaluate the reconstruction quality under different cloud coverages and various types of ground cover, providing a solid foundation for restoring satisfactory sensory effects and reliable semantic application value. In comparison to the current representative algorithms, the presented approach exhibits effectiveness and advancement in reconstructing missing regions with stability. The project is accessible at: https://github.com/RSIIPAC/LuojiaSET-OSFCR.

Tropical forest robust 3D description using advanced multidimensional SAR imaging: techniques and performance in the context of the upcoming BIOMASS mission

Tropical forest robust 3D description using advanced multidimensional SAR imaging: techniques and performance in the context of the upcoming BIOMASS mission

Polarimetric synthetic aperture radar image classification for accurate landcover mapping with advanced encoder–decoder networks using multi-dimensional probabilistic voting ensemble

Polarimetric synthetic aperture radar image classification for accurate landcover mapping with advanced encoder–decoder networks using multi-dimensional probabilistic voting ensemble

U-net with ResNet-34 backbone for dual-polarized C-band baltic sea-ice SAR segmentation

Abstract In this study, the U-net with ResNet-34, i.e. a residual neural network with 34 layers, backbone semantic segmentation network is applied to C-band sea-ice SAR imagery over the Baltic Sea. Sentinel-1 Extra Wide Swath mode HH/HV-polarized SAR data acquired during the winter season 2018–2019, and corresponding segments derived from the daily Baltic Sea ice charts were used for training the segmentation algorithm. C-band SAR image mosaics of the winter season 2020–2021 were then used to evaluate the segmentation. The major objective was to study the suitability of semantic segmentation of SAR imagery for automated SAR segmentation and also to find a potential replacement for the outdated iterated conditional modes (ICM) algorithm currently in operational use. The results compared to the daily Baltic Sea ice charts and the operational ICM segmentation and visual interpretation were encouraging from the operational point of view. Open water areas were located very well and the oversegmentation produced by ICM was significantly reduced. The correspondence between the ice chart polygons and the segmentation results was also reasonably good. Based on the results, the studied method is a potential candidate to replace the operational ICM SAR segmentation used in the Copernicus Marine Service automated sea-ice products at Finnish Meteorological Institute.

Deep network based approaches to mitigate seasonal effects in SAR images for deforestation monitoring

Abstract. Most deforestation monitoring programs are based on optical images, which are severely affected by clouds, especially in tropical regions. As an alternative, Synthetic Aperture Radar (SAR) data are minimally affected by atmospheric conditions. However, one of the challenges inherent in such approaches is the effect of seasonal rainforest variations over the SAR images. Recently proposed stabilization algorithms mitigate this effect with significant accuracy gains, however, at the cost of higher computational resources, needed to accommodate long temporal SAR image sequences. This work addresses this issue and presents two alternative solutions that attain similar or better accuracy at a much lower computational requirements. One solution is based on a ResUnet, while the second combines an LSTM and a UNet. Both approaches were tested using raw and stabilized pairs as well as raw sequences. Experiments have indicated that both approaches can mitigate the seasonality effect using a much shorter SAR image sequences than modern stabilization methods require. The results with the multitemporal input outperformed those with the preprocessed bitemporal set by 4.3% in Recall, 1.7% in Precision, and 2.7% in F1-Score, also delivering the best deforestation probability maps.

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.

Unsupervised PolSAR Image Classification Based on Superpixel Pseudo-Labels and a Similarity-Matching Network

Supervised polarimetric synthetic aperture radar (PolSAR) image classification demands a large amount of precisely labeled data. However, such data are difficult to obtain. Therefore, many unsupervised methods have been proposed for unsupervised PolSAR image classification. The classification maps of unsupervised methods contain many high-confidence samples. These samples, which are often ignored, can be used as supervisory information to improve classification performance on PolSAR images. This study proposes a new unsupervised PolSAR image classification framework. The framework combines high-confidence superpixel pseudo-labeled samples and semi-supervised classification methods. The experiments indicated that this framework could achieve higher-level effectiveness in unsupervised PolSAR image classification. First, superpixel segmentation was performed on PolSAR images, and the geometric centers of the superpixels were generated. Second, the classification maps of rotation-domain deep mutual information (RDDMI), an unsupervised PolSAR image classification method, were used as the pseudo-labels of the central points of the superpixels. Finally, the unlabeled samples and the high-confidence pseudo-labeled samples were used to train an excellent semi-supervised method, similarity matching (SimMatch). Experiments on three real PolSAR datasets illustrated that, compared with the excellent RDDMI, the accuracy of the proposed method was increased by 1.70%, 0.99%, and 0.8%. The proposed framework provides significant performance improvements and is an efficient method for improving unsupervised PolSAR image classification.

Flood inundation mapping in SAR images based on nonlocal polarization combination features

Flood inundation mapping in SAR images based on nonlocal polarization combination features

Water-body detection from synthetic aperture radar images using dual-branch fusion network

Water-body detection from synthetic aperture radar images using dual-branch fusion network