Search Results (4,178)

A deployable reflector antenna (DR-A) is a structure that can be stored in a large-diameter Synthetic Aperture Radar (SAR) antenna and be mounted onto a launch vehicle. Considering the performance of the launch vehicle, it is necessary to develop a lightweight, high-performance antenna structure. The solid-type deployable reflector antenna is composed of a number of unit main reflectors. To reduce the weight of the antenna, a lightweight main reflector must be developed. In this paper, following “Development of Deployable Reflector Antenna for the SAR Satellite (Part 1)”, the manufacturing and qualification of the main reflector using honeycomb sandwich composites are described. Four types of composite main reflectors were manufactured with variables in the manufacturing process. The manufacturing variables include the curing process of the structure, the application of an adhesive film between the sheet and the core, and the venting path inside of the sandwich core. After manufacturing the main reflector, we performed weight measurements, non-destructive testing (NDT), surface error measurement using a Coordinate Measurement Machine (CMM), and modal testing for each type of composite main reflector. Through the research and development process, we found that a perforated hole is necessary when excluding the adhesive film during bonding of an aramid core and a CFRP sheet, and a lightweight composite reflector could be developed through this process. We selected the main reflector with the best performance and developed a composite main reflector that can be applied to satellites. Full article

At present, the majority of deep learning-based ship object detection algorithms concentrate predominantly on enhancing recognition accuracy, often overlooking the complexity of the algorithm. These complex algorithms demand significant computational resources, making them unsuitable for deployment on resource-constrained edge devices, such as airborne and spaceborne platforms, thereby limiting their practicality. With the purpose of alleviating this problem, a lightweight and high-accuracy synthetic aperture radar (SAR) ship image detection network (LHSDNet) is proposed. Initially, GhostHGNetV2 was utilized as the feature extraction network, and the calculation amount of the network was reduced by GhostConv. Next, a lightweight feature fusion network was designed to combine shallow and deep features through lightweight convolutions, effectively preserving more information while minimizing computational requirements. Lastly, the feature extraction module was integrated through parameter sharing, and the detection head was lightweight to save computing resources further. The results from our experiments demonstrate that the proposed LHSDNet model increases mAP50 by 0.7% in comparison to the baseline model. Additionally, it illustrates a pronounced decrease in parameter count, computational demand, and model file size by 48.33%, 51.85%, and 41.26%, respectively, when contrasted with the baseline model. LHSDNet achieves a balance between precision and computing resources, rendering it more appropriate for edge device implementation. Full article

Accurate estimation of forest aboveground carbon stock (AGC) is essential for understanding carbon accounting and climate change. In previous studies, the extracted factors, such as spectral textures, vegetation indices, and textural features, were used to estimate the AGC. However, few studies examined how different factors affect estimation accuracy in detail. Meanwhile, there are also many uncertainties in the collection and processing of the field data. To quantify the various uncertainties in the process of AGC estimation, we used the random forest (RF) to establish estimation models based on field data and Sentinel-1/2 images in Shangri-La. The models included the band information model (BIM), the vegetation index model (VIM), the texture information model (TIM), the Sentinel-2 factor model (S-2M), and the Sentinel-1/2 factor model (S-1/2M). Then, uncertainties resulting from the plot scale and estimation models were calculated using error equations. Our goal is to analyze the influence of different factors on AGC estimation and to assess the uncertainty of plot scale and estimation models quantitatively. The results showed that (1) the uncertainty of the measurement was 3.02%, while the error of the monocarbon stock model was the main uncertainty at the plot scale, which was 9.09%; (2) the BIM had the lowest accuracy (R² = 0.551) and the highest total uncertainty (22.29%); by gradually introducing different factors in the process of modeling, the accuracies improved significantly (VIM: R² = 0.688, TIM: R² = 0.715, S-2M: R² = 0.826), and the total uncertainty decreased to some extent (VIM: 14.12%, TIM: 12.56%, S-2M: 10.79%); (3) the S-1/2M with the introduction of Sentinel-1 synthetic aperture radar (SAR) data has the highest accuracy (R² = 0.872) and the lowest total uncertainty (8.43%). The inaccuracy of spectral features is highest, followed by vegetation indices, while textural features have the lowest inaccuracy. Uncertainty in the remote-sensing-based estimation model remains a significant source of uncertainty compared to the plot scale. Even though the uncertainty at the plot scale is relatively small, this error should not be ignored. The uncertainty in the estimation process could be further reduced by improving the precision of the measurement and the fitting of the monocarbon stock estimation model. Full article

Automotive millimeter-wave (MMW) synthetic aperture radar (SAR) systems can achieve high-resolution images of detection areas, providing environmental perceptions that facilitate intelligent driving. However, curved path is inevitable in complex urban road environments. Non-uniform spatial sampling, brought about by curved path, leads to cross-coupling and spatial variation deteriorates greatly, significantly impacting the imaging results. To deal with these issues, we developed an Extended Omega-K Algorithm (EOKA) for an automotive SAR with a curved path. First, an equivalent range model was constructed based on the relationship between the range history and Doppler frequency. Then, using azimuth time mapping, the echo data was reconstructed with a form similar to that of a uniform linear case. As a result, an analytical two-dimensional (2D) spectrum was easily derived without using of the method of series reversion (MSR) that could be exploited for EOKA. The results from the parking lot, open road, and obstacle experimental scenes demonstrate the performance and feasibility of an MMW SAR for environmental perception. Full article

Flood is one of the most frequent natural disasters in the Yangtze River Basin. Flood risk evaluation is of great social significance, especially for large hydrological systems. Rainfall is both temporal and spatial, influencing surface hydrological activities. The water body range is the final outcome of a flood and can be observed from synthetic aperture radar (SAR) images under any weather condition. A flood severity evaluation model is proposed to quantitatively evaluate the flood based on water body range from area disparity and flood duration. Large hydrological objects usually span a wide range and have significant differences. This results in different initial water areas in each region. This approach addresses the issue through normalization processing. In this paper, Sentinel-1 data are used to extract the temporal water body using the adaptive bimodal method, and the water level data were also incorporated to improve the observation frequency for water area. The flood severity evaluation approach can be used to assess flood risk between any region of large hydrological systems or any flood event, regardless of their regional spatial differences and rainfall duration differences. The results show that: (1) In general, the average water body area in 2020 was 20.40% larger than it was in 2019, and the daily water body areas in 2020 were all greater than the average of 2019 with 71.36% of the days in 2020 having an area greater than the maximum in 2019. The flood severity in 2020 was 1.75 times as much as that of 2019; (2) Reach performance indexes in 2020 were in order of Yueyang (2.21) > Jiujiang (2.04) > Hankou (1.44) > Chizhou (1.32), which were inconsistent with the spatial site; (3) Flood event impact indexes in 2020 were in order as No.2 (1.64) > No.3 (1.61) > No.1 (1.44) > No.4 (1.17) > No.5 (1.15); (4) The flood was more likely the result of cumulative rainfall for 30 days. Full article

Image Implicit Neural Representations (INRs) adopt a neural network to learn a continuous function for mapping the pixel coordinates to their corresponding values. This task has gained significant attention for representing images in a continuous manner. Despite substantial progress regarding natural images, there is little investigation of INRs for Synthetic Aperture Radar (SAR) images. This work takes a pioneering effort to study INRs for SAR images and finds that fine details are hard to represent. It has been shown in prior works that fine details can be easier to learn when the model weights are better initialized, which motivated us to investigate the benefits of activating the model weight before target training. The challenge of this task lies in the fact that SAR images cannot be used during the model activation stage. To this end, we propose exploiting a cross-pixel relationship of the model output, which relies on no target images. Specifically, we design a novel self-activation method by alternatively using two loss functions: a loss used to smooth out the model output, and another used for the opposite purpose. Extensive results on SAR images empirically show that our proposed method helps improve the model performance by a non-trivial margin. Full article

Implicit neural representations (INRs) are a new way to represent all kinds of signals ranging from 1D audio to 3D shape signals, among which 2D images are the most widely explored due to their ubiquitous presence. Image INRs utilize a neural network to learn a continuous function that takes pixel coordinates as input and outputs the corresponding pixel values. The continuous representation of synthetic aperture radar (SAR) images using INRs has not yet been explored. Existing INR frameworks developed on natural images show reasonable performance, but this performance suffers when capturing fine details. This can be attributed to INR’s prioritization of learning inter-pixel relationships, which harms intra-pixel mapping in those regions that require fine detail. To address this, we decompose the target image into an artificial uniform noise component (intra-pixel mapping) and a residual image (inter-pixel relationships). Rather than directly learning the INRs for the target image, we propose a noise-first residual learning (NRL) method. The NRL first learns the uniform noise component, then gradually incorporates the residual into the optimization target using a sine-adjusted incrementation scheme as training progresses. Given that some SAR images inherently contain significant noise, which can facilitate learning the intra-pixel independent mapping, we propose a gradient-based dataset separation method. This method distinguishes between clean and noisy images, allowing the model to learn directly from the noisy images. Extensive experimental results show that our method achieves competitive performance, indicating that learning the intra-pixel independent mapping first, followed by the inter-pixel relationship, can enhance model performance in learning INR for SAR images. Full article

The Fucheng-1 (FC-1) satellite has successfully transitioned from its initial operational phase and is now undergoing a detailed performance assessment for time-series deformation monitoring. This study evaluates the surface deformation monitoring capabilities of the newly launched FC-1 satellite using the interferometric synthetic aperture radar (InSAR) technique, particularly in urban applications. By analyzing the observation data from 20 FC-1 scenes and 20 Sentinel-1 scenes, deformation velocity maps of a university in Mianyang city were obtained using persistent scatterer interferometry (PSI) and distributed scatterer interferometry (DSI) techniques. The results show that thanks to the high resolution of 3 × 3 m of the FC-1 satellite, significantly more PS points and DS points were detected than those detected by Sentinel-1, by 13.4 times and 17.9 times, respectively. The distribution of the major deformation areas detected by both satellites in the velocity maps is generally consistent. FC-1 performs better than Sentinel-1 in monitoring densely structured and vegetation-covered areas. Its deformation monitoring capability at the millimeter level was further validated through comparison with leveling measurements, with average errors and root mean square errors of 1.761 mm and 2.172 mm, respectively. Its high-resolution and high-precision interferometry capabilities make it particularly promising in the commercial remote sensing market. Full article

This article introduces a small microwave remote sensing satellite weighing 310 kg, operating in low earth orbit (LEO). It is equipped with an X-band synthetic aperture radar (SAR) antenna, capable of a maximum imaging resolution of 0.6 m. To achieve the objectives of lower cost, reduced weight, minimized power consumption, and enhanced temperature stability, an optimized thermal design method tailored for satellites has been developed, with a particular focus on SAR antennas. The thermal control method of the antenna is closely integrated with structural design, simplifying the thermal design and its assembly process, reducing the resource consumption of thermal control systems. The distribution of thermal interface material (TIM) in the antenna assembly has been carefully calculated, achieving a zero-consumption thermal design for the SAR antenna. And the temperature difference of the entire antennas when powered on and powered off would not exceed 17 °C, meeting the specification requirements. In addition, to ensure the accuracy of antenna pointing, the support plate of antennas requires stable temperature. The layout of the heaters on the board has been optimized, reducing the use of heaters by 30% while ensuring that the temperature variation of the support board remains within 5 °C. Then, an on-orbit thermal simulation analysis of the satellite was conducted to refine the design and verification. Finally, the thermal test of the SAR satellite under vacuum conditions was conducted, involving operating the high-power antenna, verifying that the peak temperature of T/RM is below 29 °C, the temperature fluctuation amplitude during a single imaging task is 10 °C, and the lowest temperature point of the support plate is 16 °C. The results of the thermal simulation and test are highly consistent, verifying the correctness and effectiveness of the thermal design. Full article

Surface deformation poses significant risks to urban infrastructure, agriculture, and the environment in many regions worldwide, including Rwanda and the neighboring areas. This study focuses on surface deformation mapping and time series analysis in Rwanda and the neighboring areas from 2 July 2016 to 8 June 2023 using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The influence of atmospheric delay error is effectively reduced by integrating the Generic Atmospheric Correction Online Service (GACOS), which provides precise atmospheric delay maps. Then the SBAS-InSAR method is utilized to generate surface deformation maps and displacement time series across the region. The results of this study indicated that the maximum deformation rate was −0.11 m/yr (subsidence) and +0.13 m/yr (uplift). Through time series analysis, we quantified subsidence and uplift areas and identified key drivers of surface deformation. Since subsidence or uplift varies across the region, we have summarized the different deformation patterns and briefly analyzed the factors that may lead to deformation. Finally, this study underscores the importance of SBAS-InSAR for tracking surface deformation in Rwanda and the neighboring areas, which offers valuable perspectives for sustainable land utilization strategizing and risk mitigation. Full article

High-performance neural networks for synthetic aperture radar (SAR) automatic target recognition (ATR) often encounter the challenge of data scarcity. The lack of sufficient labeled SAR image datasets leads to the consideration of using simulated data to supplement the dataset. On the one hand, electromagnetic computation simulations provide high amplitude accuracy but are inefficient for large-scale datasets due to their complex computations and physical models. On the other hand, ray tracing simulations offer high geometric accuracy and computational efficiency but struggle with low amplitude correctness, hindering accurate numerical feature extraction. Furthermore, the emergence of generative adversarial networks (GANs) provides a way to generate simulated datasets, trying to balance computational efficiency with image quality. Nevertheless, the simulated SAR images generated based on random noise lack constraints, and it is also difficult to generate images that exceed the parameter conditions of the real image’s training set. Hence, it is essential to integrate physics-based simulation techniques into GANs to enhance the generalization ability of the imaging parameters. In this paper, we present the SingleScene-SAR Simulator, an efficient framework for SAR image simulation that operates under limited real SAR data. This simulator integrates rasterized shooting and bouncing rays (SBR) with cycle GAN, effectively achieving both amplitude correctness and geometric accuracy. The simulated images are appropriate for augmenting datasets in target recognition networks. Firstly, the SingleScene-SAR Simulator employs a rasterized SBR algorithm to generate radar cross section (RCS) images of target models. Secondly, a specific training pattern for cycle GAN is established to translate noisy RCS images into simulated SAR images that closely resemble real ones. Finally, these simulated images are utilized for data augmentation. Experimental results based on the constructed dataset show that with only one scene SAR image containing 30 target chips, the SingleScene-SAR Simulator can efficiently produce simulated SAR images that exhibit high similarity in both spatial and statistical distributions compared with real images. By employing simulated SAR images for data augmentation, the accuracy of target recognition networks can be consistently and significantly enhanced. Full article

Multiple-input multiple-output synthetic aperture radar (MIMO-SAR) anti-jamming waveform design relies on accurate prior information about the interference. However, it is difficult to obtain accurate prior knowledge about uncertain intermittent sampling repeater jamming (ISRJ), leading to a severe decline in the detection performance of MIMO-SAR systems. Therefore, this article studies the robust joint design problem of MIMO radar transmit waveform and filter against uncertain ISRJ. We characterize two categories of uncertain interference, including sample length uncertainty and sample-time uncertainty, modeled as Gaussian distribution in different range bins. Based on the uncertain interference model, we formulate the maximizing SINR as a figure of merit, which is a non-convex quadratic optimization problem under specific waveform constraints. Based on the alternating direction method of multipliers (ADMM) framework, a novel joint design algorithm of waveform and filter is proposed. In order to improve the convergence performance of ADMM, the difference in convex functions (DC) programming is applied to the ADMM iterations framework to solve the problem of waveform energy inequality constraint. Finally, numerical results demonstrate the effectiveness and robustness of the proposed method, compared to the existing methods that utilize deterministic interference models in the uncertain ISRJ environment. Moreover, the spaceborne SAR real scene imaging simulations are conducted to evaluate the anti-ISRJ performance. Full article

Glacier movement is an important indicator of climate change, reflecting the quality and state changes in glacier migration and mass balance in the context of global warming. Although accurately estimating glacier surface flow velocity is crucial for various applications, achieving this is challenging due to factors such as low temporal correlation and high noise effects. This paper presents the pixel offset tracking (POT) technology based on Synthetic Aperture Radar (SAR) data for glacier velocity monitoring, with enhanced cross-correlation matching window and noise suppression approaches. In particular, a noise suppression optimization method and a matching window optimization index suitable for wide-area glacier velocity monitoring are proposed. The inter-annual wide-area two-dimensional plane flow velocity of glaciers in the Svalbard archipelago was obtained by using a total of seven Sentinel-1 data sets from two orbits covering the entire Svalbard archipelago in 2021. The results indicate that 25 large glaciers in Svalbard destabilized in 2021, with a peak flow velocity of 6.18 m/day. At the same time, the influence of climate, topography, and other factors on glacier surface velocity is discussed. The wide-area glacier velocity monitoring method and its application demonstrated in this paper will serve as a valuable reference for studying glacier migration in the Arctic Svalbard archipelago and for other large-scale wide-area deformation monitoring efforts. Full article

Floods, one of the costliest, and most frequent hazards, are expected to worsen in the U.S. due to climate change. The real-time forecasting of flood inundations is extremely important for proactive decision-making to reduce damage. However, traditional forecasting methods face challenges in terms of implementation and scalability due to computational burdens and data availability issues. Current forecasting services in the U.S. largely rely on hydrodynamic modeling, limited to river reaches near in situ gauges and requiring extensive data for model setup and calibration. Here, we have successfully adapted the Forecasting Inundation Extents using REOF (FIER) analysis framework to produce forecasted water fraction maps in two U.S. flood-prone regions, specifically the Red River of the North Basin and the Upper Mississippi Alluvial Plain, utilizing Visible Infrared Imaging Radiometer Suite (VIIRS) optical imagery and the National Water Model. Comparing against historical VIIRS imagery for the same dates, FIER 1- to 8-day medium-range pseudo-forecasts show that about 70–80% of pixels exhibit absolute errors of less than 30%. Although originally developed utilizing Synthetic Aperture Radar (SAR) images, this study demonstrated FIER’s versatility and effectiveness in flood forecasting by demonstrating its successful adaptation with optical VIIRS imagery which provides daily water fraction product, offering more historical observations to be used as inputs for FIER during peak flood times, particularly in regions where flooding commonly happens in a short period rather than following a broad seasonal pattern. Full article

Synthetic aperture radar is widely applied to ship detection due to generating high-resolution images under diverse weather conditions and its penetration capabilities, making SAR images a valuable data source. However, detecting multi-scale ship targets in complex backgrounds leads to issues of false positives and missed detections, posing challenges for lightweight and high-precision algorithms. There is an urgent need to improve accuracy of algorithms and their deployability. This paper introduces LH-YOLO, a YOLOv8n-based, lightweight, and high-precision SAR ship detection model. We propose a lightweight backbone network, StarNet-nano, and employ element-wise multiplication to construct a lightweight feature extraction module, LFE-C2f, for the neck of LH-YOLO. Additionally, a reused and shared convolutional detection (RSCD) head is designed using a weight sharing mechanism. These enhancements significantly reduce model size and computational demands while maintaining high precision. LH-YOLO features only 1.862 M parameters, representing a 38.1% reduction compared to YOLOv8n. It exhibits a 23.8% reduction in computational load while achieving a mAP50 of 96.6% on the HRSID dataset, which is 1.4% higher than YOLOv8n. Furthermore, it demonstrates strong generalization on the SAR-Ship-Dataset with a mAP50 of 93.8%, surpassing YOLOv8n by 0.7%. LH-YOLO is well-suited for environments with limited resources, such as embedded systems and edge computing platforms. Full article