Synthetic Aperture Radar Image Data Research Articles

GPU Acceleration for SAR Satellite Image Ortho-Rectification

Synthetic Aperture Radar (SAR) satellite image ortho-rectification requires pixel-level calculations, which are time-consuming. Moreover, for SAR images with large overlapping areas, the processing time for ortho-rectification increases linearly, significantly reducing the efficiency of SAR satellite image mosaic. This paper thoroughly analyzes two geometric positioning models for SAR images. In order to address the high computation time of pixel-by-pixel ortho-rectification in SAR satellite images, a GPU-accelerated pixel-by-pixel correction method based on a rational polynomial coefficients (RPCs) model is proposed, which improves the efficiency of SAR satellite image ortho-rectification. Furthermore, in order to solve the problem of linearly increasing processing time for the ortho-rectification of multiple SAR images in large overlapping areas, a multi-GPU collaborative acceleration strategy for the ortho-rectification of multiple SAR images in large overlapping areas is proposed, achieving efficient ortho-rectification processing of multiple SAR image data in large overlapping areas. By conducting ortho-rectification experiments on 20 high-resolution SAR images from the Gaofen-3 satellite, the feasibility and efficiency of the multi-GPU collaborative acceleration processing algorithm are verified.

A Novel Multimodal Fusion Framework Based on Point Cloud Registration for Near-Field 3D SAR Perception

This study introduces a pioneering multimodal fusion framework to enhance near-field 3D Synthetic Aperture Radar (SAR) imaging, crucial for applications like radar cross-section measurement and concealed object detection. Traditional near-field 3D SAR imaging struggles with issues like target–background confusion due to clutter and multipath interference, shape distortion from high sidelobes, and lack of color and texture information, all of which impede effective target recognition and scattering diagnosis. The proposed approach presents the first known application of multimodal fusion in near-field 3D SAR imaging, integrating LiDAR and optical camera data to overcome its inherent limitations. The framework comprises data preprocessing, point cloud registration, and data fusion, where registration between multi-sensor data is the core of effective integration. Recognizing the inadequacy of traditional registration methods in handling varying data formats, noise, and resolution differences, particularly between near-field 3D SAR and other sensors, this work introduces a novel three-stage registration process to effectively address these challenges. First, the approach designs a structure–intensity-constrained centroid distance detector, enabling key point extraction that reduces heterogeneity and accelerates the process. Second, a sample consensus initial alignment algorithm with SHOT features and geometric relationship constraints is proposed for enhanced coarse registration. Finally, the fine registration phase employs adaptive thresholding in the iterative closest point algorithm for precise and efficient data alignment. Both visual and quantitative analyses of measured data demonstrate the effectiveness of our method. The experimental results show significant improvements in registration accuracy and efficiency, laying the groundwork for future multimodal fusion advancements in near-field 3D SAR imaging.

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model.

FCCD-SAR: A Lightweight SAR ATR Algorithm Based on FasterNet.

In recent times, the realm of remote sensing has witnessed a remarkable surge in the area of deep learning, specifically in the domain of target recognition within synthetic aperture radar (SAR) images. However, prevailing deep learning models have often placed undue emphasis on network depth and width while disregarding the imperative requirement for a harmonious equilibrium between accuracy and speed. To address this concern, this paper presents FCCD-SAR, a SAR target recognition algorithm based on the lightweight FasterNet network. Initially, a lightweight and SAR-specific feature extraction backbone is meticulously crafted to better align with SAR image data. Subsequently, an agile upsampling operator named CARAFE is introduced, augmenting the extraction of scattering information and fortifying target recognition precision. Moreover, the inclusion of a rapid, lightweight module, denoted as C3-Faster, serves to heighten both recognition accuracy and computational efficiency. Finally, in cognizance of the diverse scales and vast variations exhibited by SAR targets, a detection head employing DyHead's attention mechanism is implemented to adeptly capture feature information across multiple scales, elevating recognition performance on SAR targets. Exhaustive experimentation on the MSTAR dataset unequivocally demonstrates the exceptional prowess of our FCCD-SAR algorithm, boasting a mere 2.72 M parameters and 6.11 G FLOPs, culminating in an awe-inspiring 99.5% mean Average Precision (mAP) and epitomizing its unparalleled proficiency.

Extracting water body data based on SDWI and threshold segmentation: A case study in permafrost area surrounding Salt Lake in Hoh Xil, Qinghai-Tibet Plateau, China

There are a large number of lakes, rivers, and other natural water bodies distributed in the permafrost area of the Qinghai-Tibet Plateau (QTP). The changes in water bodies will affect the distribution of water resources in surrounding areas and downstream areas, resulting in environmental impact and bringing potential flood disasters, which will induce more serious issues and problems in alpine and high-altitude areas with a fragile habitat (such as the QTP in China). Generally, effective, reasonable, and scientific monitoring of large-scale water bodies can not only document the changes in water bodies intuitively, but also provide important theoretical reference for subsequent environmental impact prediction, and disaster prevention and mitigation in due course of time. The large-scale water extraction technology derived from the optical remote sensing (RS) image is seriously affected by clouds, bringing about large differences among the extracted water result products. Synthetic aperture radar (SAR) RS technology has the unique advantage characteristics of all-weather, all-day, strong penetration, and not being affected by clouds, which is hopeful in extracting water body data, especially for days with cloudy weather. The data extraction of large-scale water bodies based on SAR images can effectively avoid the errors caused by clouds that become prevalent at present. In this paper, the Hoh Xil Salt Lake on the QTP and its surrounding five lakes are taken as the research objects. The 2-scene Sentinel-1 SAR image data covering the whole area on 22 August 2022 was used to verify the feasibility of extracting water body data in permafrost zones. Furthermore, on 22 August 2022, the wealth here was cloudy, which made the optical RS images, e.g., Sentinel-2 images full of clouds. The results show that: using the Sentinel-1 image and threshold segmentation method to extract water body data is efficient and effective with excellent results in permafrost areas. Concretely, the Sentinel-1 dual-polarized water index (SDWI), calculated by combining dual vertical–vertical (VV) polarized and vertical–horizontal (VH) polarized data is a useful index for water extraction and the result is better than each of the VV or VH polarized images.

Wave retrieval from quad-polarized Chinese Gaofen-3 SAR image using an improved tilt modulation transfer function

ABSTRACT An accurate Modulation Transfer Function (MTF) is essential for Synthetic Aperture Radar (SAR) wave spectra retrieval. This study aimed to investigate the performance of a quad-polarized wave retrieval algorithm based on fully polarimetric SAR image data using the improved tilt MTF and considering the influence of wind speed. The tilt MTF is the key factor in the wave retrieval scheme from quad-polarized (Vertical–Vertical (VV), Horizontal–Horizontal (HH), Vertical–Horizontal (VH), and Horizontal–Vertical (HV)) SAR images. In this study, the waves were inverted from more than 1300 Gaofen-3 (GF-3) images acquired in quad-polarization strip mode with a spatial resolution of 16 m and a swath coverage of 50 km. The winds were retrieved using the Geophysical Model Function (GMF) C-band SAR model for Gaofen-3 (CSARMOD-GF), which is suitable for re-calibrated GF-3 images in VV-polarization. The comparison of the wind speed yielded a Root Mean Square Error (RMSE) of 1.73 m/s and a Correlation Coefficient (COR) of 0.94. The validation of the Significant Wave Height (SWH) simulated using the Waves Nearshore (SWAN) model against Haiyang-2B (HY-2B) altimeter data yielded an RMSE of 0.56 m and a COR of 0.87. The results reveal that the SAR-derived wind and SWAN-simulated SWH are suitable for analysis of SAR wave retrieval. The full polarimetric technique was applied to the collected images, and the statistical analysis yielded a RMSE of 0.51 m, a COR of 0.75, and a Scatter Index (SI) of 0.44 compared with the SWHs retrieved using the simulations from the SWAN model. The non-polarized contribution in the Normalized Radar Cross Section (NRCS; unit: dB) caused by wave breaking was calculated using a theoretical approach that employs the VV-polarized calibrated NRCS and HH-polarized calibrated NRCS. The effect of wave breaking on the SAR retrieval waves was studied. The bias (SAR-derived minus SWAN-simulated SWH) increased as the (/) ratio (>0.4) increased, and the accuracy improved when the ratio was less than 0.4. This behavior is reasonable since the wave breaking inevitably affects the tilt modulation. Therefore, wave breaking should be considered in SAR wave retrieval using the approach proposed in this paper under extreme sea states, such as typhoons and hurricanes.

An Improved SAR Image Semantic Segmentation Deeplabv3+ Network Based on the Feature Post-Processing Module

Synthetic Aperture Radar (SAR) can provide rich feature information under all-weather and day-night conditions because it is not affected by climatic conditions. However, multiplicative speckle noise exists in SAR images, which makes it difficult to accurately identify some fuzzy targets in SAR images, such as roads and rivers, during semantic segmentation. This paper proposes an improved Deeplabv3+ network that can be effectively applied to the semantic segmentation task of SAR images. Firstly, this paper added the attention mechanism and, combined with the idea of an image pyramid, proposed the Feature Post-Processing Module (FPPM) to post-process the network output feature map, obtain better fine image features, and solve the problem of fuzzy texture and spectral features of SAR images. Compared to the original Deeplabv3+ network, the segmentation accuracy has been improved by 3.64% and mIoU improved by 1.09%. Secondly, to solve the problems of limited SAR image data and an unbalanced sample, this paper used the focal loss function to improve the backbone function of the network, which increased the mIoU by 1.01%. Finally, the Atrous Spatial Pyramid Pooling (ASPP) module was improved and the 3 × 3 void convolution in ASPP was decomposed into 2D, which can maintain the void ratio and effectively reduce the calculation amount of the module, shorten the training time by 19 ms and improve the semantic segmentation effect.

High-Precision Inversion of Shallow Bathymetry under Complex Hydrographic Conditions Using VGG19—A Case Study of the Taiwan Banks

Shallow bathymetry is important for ocean exploration, and the development of high-precision bathymetry inversion methods, especially for shallow waters with poor quality, is a major research aim. Synthetic aperture radar (SAR) image data benefit from a wide coverage, high measurement density, rapidity, and low consumption but are limited by low accuracy. Alternatively, multibeam data have low coverage and are difficult to obtain but have a high measurement accuracy. In this paper, taking advantage of the complementary properties, we use SAR image data as the content map and multibeam images as the migrated style map, applying the VGG19 neural network (optimizing the loss function formula) for bathymetric inversion. The model was universal and highly accurate for bathymetric inversion of shallow marine areas, such as turbid water in Taiwan. There was a strong correlation between bathymetric inversion data and measured data (R2 = 0.8822; RMSE = 1.86 m). The relative error was refined by 9.22% over those of previous studies. Values for different bathymetric regions were extremely correlated in the region of 20–40 m. The newly developed approach is highly accurate over 20 m in the open ocean, providing an efficient, precise shallow bathymetry inversion method for complex hydrographic conditions.

Statistical Adaptation Loss Improved SMALL Sample Ship Detection Method Based on an Attention Mechanism and Data Enhancement

Synthetic aperture radar (SAR) imagery is a promising data source for ocean activity detection. Ship target detection based on SAR images is widely used in maritime trade and the military. SAR image data are rare, and the amount of public data is small. For applications of SAR image ship target detection, a model with low data dependence, fast iteration and low training cost is needed. In this paper, the balanced positive and negative data enhancement method was used. Through statistical analysis of the training dataset, similar sea areas in the training set are filled with detection targets with comfortable size features. Increasing the proportion of positive samples in the data helps to improve the model detection effect. The regional attention preadaptation mechanism based on statistical analysis was implemented to extract information, and the scale-adaptive loss was combined to improve the detection accuracy of the model. Using the same data, our model exhibited better performance. When using 30% of the data, our model was stable in terms of accuracy and average precision (AP) and maintained detection results similar to the training results achieved using 100% of the dataset.

Conversion from multi-spectral data into SAR data with Deep Convolution Neural Architecture using Generative Adversarial Network

Synthetic Aperture Radar (SAR) is a high-resolution radar extensively used for aerial and terrestrial images in numerous applications. The SAR images are all weather and day/night capable. It is extensively used in defense/military systems for surveillance, target detection and target tracking. In current times, the need for deep learning algorithms has increased due to its capability of generating SAR images, as it is very intensive and expensive task for the acquisition and formation of SAR images through sensors. Thus, alternative methods are required to produce SAR image data. In this paper, generative adversarial networks (GAN) are used to generate SAR images from a limited pre-existing dataset. This paper presents a novel way of translating optical images to SAR images, for the generation of paired, unpaired data set. Due to the unavailability of an open-source paired, unpaired image dataset, a synthetic paired image dataset is created for proving the concepts of CycleGAN and Pix-to-Pix algorithm by fine tuning the algorithms using generated datasets. The paper tried tackling this problem in two ways, both paired and unpaired image-to-image translation.

HRLE-SARDet: A Lightweight SAR Target Detection Algorithm Based on Hybrid Representation Learning Enhancement

In recent years, deep learning has been widely used in remote sensing, especially in the field of synthetic aperture radar (SAR) image target detection. However, all of these deep learning models continue increasing the network’s depth and width without maintaining a good balance between accuracy and speed. Therefore, in this paper, we propose a hybrid representation learning-enhanced SAR target detection algorithm based on the unique features of SAR images from a lightweight perspective called HRLE-SARDet. First, we design a lightweight and scattering feature extraction backbone that is more suitable for SAR image data. Second, for the multiscale feature discrepancy, we design a new multiscale feature fusion neck. Next, to better extract the scattering information from small targets of SAR images and improve the detection accuracy, we design a lightweight hybrid representation learning enhancement module. Finally, to better fit target detection for SAR image datasets, we redesign a more flexible loss function, which allows for an easy adjustment of the importance of polynomial bases according to the target task and dataset. Extensive experimental results on three SAR image ship target datasets (SSDD, AIR-SARShip-2.0, and HRSID) and a newly released large multiclass target SAR dataset (MSAR-1.0) show that our HRLE-SARDet achieves 98.4%, 79.2%, 92.5%, and 88.4% mAPs (mean Average Precision) with only 1.09 M parameters and 2.5 G floating-point operations (FLOPs) on the SSDD, AIR-SARShip-2.0, HRSID, and MSAR-1.0 datasets, which is an excellent performance.

SAR Image Compression Based on Multi-Resblock and Global Context

The synthetic aperture radar(SAR) image is widely used in many remote sensing applications. In order to store and transmit the increasing SAR image data, more efficient compression algorithms are needed. The purpose of this paper is to introduce a new framework for compressing SAR images. Firstly, we propose a novel analysis and synthesis transform based on multi-Resblocks for transforming the original SAR image into a compact latent representation. Then, a Gaussian mixture model is used to estimate the latent representation’s distribution. In order to explore the redundancy within the latent representation, the entropy model parameter is estimated by combining the local context, global context, and hyperprior information. In order to evaluate the performance of the proposed algorithm, we conduct experiments on a dataset of SAR images. The results show that the proposed algorithm outperforms JPEG2000 and some state-of-the-art learned image compression schemes in terms of compression performance.

DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation

Successful implementation of oil spill segmentation in synthetic aperture radar (SAR) images is vital for marine environmental protection. In this article, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images and the other is the generative module to produce oil spill segmentation maps by drawing the latent feature variables as inputs. Thus, to yield accurate segmentation, we take into account the intrinsic distribution of backscatter values in SAR images and embed it in our segmentation model. The intrinsic distribution originates from SAR imagery, describing the physical characteristics of oil spills. In the training process, the formulated intrinsic distribution guides efficient learning of optimal latent feature variable inference for oil spill segmentation. The efficient learning enables the training of our proposed DGNet with a small amount of image data. This is economically beneficial to oil spill segmentation where the availability of oil spill SAR image data is limited in practice. Additionally, benefiting from optimal latent feature variable inference, our proposed DGNet performs accurate oil spill segmentation. We evaluate the segmentation performance of our proposed DGNet with different metrics, and experimental evaluations demonstrate its effective segmentations.

Theoretical Analysis of an Adaptive Periodic Multi Installment Scheduling With Result Retrieval for SAR Image Processing

Processing a large-scale Synthetic Aperture Radar (SAR) image dataset on a distributed computing infrastructure poses a challenging problem. Large-scale load distribution strategies like multi-installment scheduling (MIS) assume that the size of the result is negligible compared to the input workloads and hence ignore it in their design. Similarly, numerical methods like particle swarm optimization and their variants are not practical for real-time applications, given their run-time complexities. As both the results retrieval and completion time are crucial for SAR image data processing, in this article, we attempt to provide a thorough theoretical analysis of an adaptive MIS that includes the result retrieval phase. We use the periodic nature of the internal installments to keep the strategy simple and fine-tune the last installment to avoid any idle times in the processors. We derive a closed-form solution for the load fractions and hence, the overall processing time, schedule feasibility criteria, and certain other properties that lead to adaptive scheduling. Finally, we validate our theoretical findings through rigorous simulation studies using a loosely connected virtual machines (VMs) topology for the SAR dataset.

A Scale-Aware Pyramid Network for Multi-Scale Object Detection in SAR Images

Multi-scale object detection within Synthetic Aperture Radar (SAR) images has become a research hotspot in SAR image interpretation. Over the past few years, CNN-based detectors have advanced sharply in SAR object detection. However, the state-of-the-art detection methods are continuously limited in Feature Pyramid Network (FPN) designing and detection anchor setting aspects due to feature misalignment and targets’ appearance variation (i.e., scale change, aspect ratio change). To address the mentioned limitations, a scale-aware feature pyramid network (SARFNet) is proposed in this study, which comprises a scale-adaptive feature extraction module and a learnable anchor assignment strategy. To be specific, an enhanced feature pyramid sub-network is developed by introducing a feature alignment module to estimate the pixel offset and contextually align the high-level features. Moreover, a scale-equalizing pyramid convolution is built through 3-D convolution within the feature pyramid to improve inter-scale correlation at different feature levels. Furthermore, a self-learning anchor assignment is set to update hand-crafted anchor assignments to learnable anchor/feature configuration. By using the dynamic anchors, the detector of this study is capable of flexibly matching the target with different appearance changes. According to extensive experiments on public SAR image data sets (SSDD and HRSID), our algorithm is demonstrated to outperform existing boat detectors.

Change Type Recognition for SAR Images Using Statistical Bidirectional LSTM Network With Few Labeled Samples

Change detection in synthetic aperture radar (SAR) images has garnered significant research interest. However, to date, only the change area can be obtained, which seriously restricts further development of SAR image applications. To solve this problem, this paper proposes a change type recognition method for SAR images based on a statistical bidirectional long short-term memory (LSTM) network. The proposed method can obtain the changed area and identify the land type change between pairs of SAR images. Considering the statistical characteristics and sample number of SAR images, change detection and type recognition are realized in a unified framework. First, feature extraction is performed through the statistical features of the original SAR image and difference image (DI). Then, a bidirectional LSTM network is used to identify the change types and obtain the change detection results under the small sample deep learning network. On two sets of medium-low resolution SAR image data and one set of GaoFen-3 high-resolution real SAR image data, our method produced high-quality results under small sample conditions.

Explorations of Contrastive Learning in the Field of Small Sample SAR ATR

Synthetic aperture radar (SAR) image recognition plays a significant role in civil and military target perception, and deep learning methods are commonly used for SAR image recognition. However, for small-sample SAR datasets, deep learning methods suffer from problems such as overfitting and gradient explosion during the training process. In this study, first, we pre-train the deep neural network on the unlabeled SAR image dataset with self-supervised contrastive learning, so as to obtain a deep feature extraction network with better representation ability. Then, we transfer the pre-trained weights to the small-sample SAR image data for fine-tuning and linear evaluation, which in turn can improve the overfitting problem of small-sample SAR image recognition. This research can give scientific and objective small-sample target recognition laws, which will promote the long development of the scientific field of deep representation learning research and can generate great economic benefits in the field of SAR image target recognition.

Multi-Class Dual- Stream Convolutional Neural Network for Synthetic Aperture Radar Automatic Target Recognition of Ground Military Vehicle

Synthetic Aperture Radar (SAR) target classification is one of the largest branches of SAR image analysis. Despite the remarkable achievements of deep learning-based SAR target prediction algorithms, current object recognition algorithms are limited in terms of military applications. Acquisition and labeling of SAR target images are time-consuming and cumbersome. Obtaining adequate training data is also challenging in many cases. Deep learning-based models are always susceptible to overfitting because of insufficient training data. This limitation prevents them from being widely used to classify SAR targets. To overcome the problem of insufficient sampling and to learn more accurate representations for SAR image recognition, we propose a two way input of SAR images into a dual stream of DCNN. Concatenating two input SAR images representations was done using the restricted raw SAR data in order to extract the integral features from the 2 input SAR images representations for classification. The proposed methodology addressed the problem of insufficient sample in SAR target classification and improved classification accuracy without overfitting. Experimental results confirmed that the proposed method is effective in addressing the problem of insufficient sample in SAR target classification. This technique can be integrated into any SAR classification model based on convolutional neural networks (CNNs). The model MCDS-CNN results in a 99.1% recognition accuracy. Despite the limited availability of SAR image data from MSTAR, this approach provides good recognition results.

Change Detection in Synthetic Aperture Radar Images Based on a Generalized Gamma Deep Belief Networks.

Change detection from synthetic aperture radar (SAR) images is of great significance for natural environmental protection and human societal activity, which can be regarded as the process of assigning a class label (changed or unchanged) to each of the image pixels. This paper presents a novel classification technique to address the SAR change-detection task that employs a generalized Gamma deep belief network (gΓ-DBN) to learn features from difference images. We aim to develop a robust change detection method that can adapt to different types of scenarios for bitemporal co-registered Yellow River SAR image data set. This data set characterized by different looks, which means that the two images are affected by different levels of speckle. Widely used probability distributions offer limited accuracy for describing the opposite class pixels of difference images, making change detection entail greater difficulties. To address the issue, first, a gΓ-DBN can be constructed to extract the hierarchical features from raw data and fit the distribution of the difference images by means of a generalized Gamma distribution. Next, we propose learning the stacked spatial and temporal information extracted from various difference images by the gΓ-DBN. Consequently, a joint high-level representation can be effectively learned for the final change map. The visual and quantitative analysis results obtained on the Yellow River SAR image data set demonstrate the effectiveness and robustness of the proposed method.

A Novel SAR Image Target Recognition Algorithm under Big Data Analysis

Synthetic aperture radar (SAR) image target recognition technology is aimed at automatically determining the presence or absence of target information from the input SAR image and improving the efficiency and accuracy of SAR image interpretation. Based on big data analysis, dirty data is removed, clean data is returned, and standardized processing of SAR image data is realized. At the same time, by establishing a statistical model of coherent speckles, the convolutional autoencoder is used to denoise the SAR image. Finally, the network model modified by softmax cross-entropy loss and Fisher loss is used for automatic target recognition. Based on the MSTAR data set, two scene graphs containing the target synthesized by the background image and the target slice are used for experiments. Several comparative experiments have verified the effectiveness of the classification and recognition model in this paper.