Search Results (4,224)

In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average peak signal-to-noise ratio (PSNR) improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate fast fourier transform (FFT) methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions. Full article

Detecting ships in Synthetic Aperture Radar (SAR) images poses a complex challenge, with recent progress primarily attributed to the development of rotated detectors. However, existing methods often neglect the crucial influence of inherent characteristics in SAR images, such as common speckle noise. Moreover, a notable gap exists in modeling diverse features, particularly the fusion of rotational and high-frequency features. To address these challenges, this paper introduces a high-accuracy detector called PRDet, which builds on two key innovations: partial differential equation (PDE)-Guided Wavelet Transform (PGWT) and Diverse Feature Learning Block (DFLB). The PGWT enhances high-frequency features, such as edges and textures, while eliminating speckle noise by optimizing wavelet transform with PDE, leveraging the ability of PDE to model local variations and preserve structural details. The DFLB, with strong expressive capability, extracts and fuses multi-form ship features through three branches, enabling more accurate ship localization. Extensive experimental evaluations on the publicly available RSSDD and SRSDD-V1.0 benchmarks demonstrate PRDet’s superiority over other SAR rotated ship detectors. For example, on the RSSDD dataset, PRDet achieves an offshore precision of 0.938 and an mAP of 0.908, confirming its effectiveness for practical maritime surveillance applications. Full article

Deep learning-based methods have shown great promise for synthetic aperture radar (SAR) automatic target recognition (ATR) in recent years. These methods demonstrate superior performance compared to traditional approaches across various recognition tasks. However, these methods often face significant challenges due to the limited availability of labeled samples, which is a common issue in SAR image analysis owing to the high cost and difficulty of data annotation. To address this issue, a variety of few-shot learning approaches have been proposed and have demonstrated promising results under data-scarce conditions. Nonetheless, a notable limitation of many existing few-shot methods is that their performance tends to plateau when more labeled samples become available. Most few-shot methods are optimized for scenarios with extremely limited data. As a result, they often fail to leverage the advantages of larger datasets. This leads to suboptimal recognition performance compared to conventional deep learning techniques when sufficient training data is available. Therefore, there is a pressing need for approaches that not only excel in few-shot scenarios but also maintain robust performance as the number of labeled samples increases. To this end, we propose a novel method, termed relation-guided embedding transductive propagation network with residual correction (RGE-TPNRC), specifically designed for few-shot SAR ATR tasks. By leveraging mechanisms such as relation node modeling, relation-guided embedding propagation, and residual correction, RGE-TPNRC can fully utilize limited labeled samples by deeply exploring inter-sample relations, enabling better scalability as the support set size increases. Consequently, it effectively addresses the plateauing performance problem of existing few-shot learning methods when more labeled samples become available. Firstly, input samples are transformed into support-query relation nodes, explicitly capturing the dependencies between support and query samples. Secondly, the known relations among support samples are utilized to guide the propagation of embeddings within the network, enabling manifold smoothing and allowing the model to generalize effectively to unseen target classes. Finally, a residual correction propagation classifier refines predictions by correcting potential errors and smoothing decision boundaries, ensuring robust and accurate classification. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) and OpenSARShip datasets demonstrate that our method can achieve state-of-the-art performance in few-shot SAR ATR scenarios. Full article

Speckle noise inherent in Synthetic Aperture Radar (SAR) images severely degrades image quality and hinders downstream tasks such as interpretation and target recognition. Existing despeckling methods, both traditional and deep learning-based, often struggle to balance effective speckle suppression with structural detail preservation. Although Denoising Diffusion Probabilistic Models (DDPMs) have shown remarkable potential for SAR despeckling, their computational overhead from iterative sampling severely limits practical applicability. To mitigate these challenges, this paper proposes the Efficient Conditional Diffusion Model (ECDM) for SAR despeckling. We integrate the cosine noise schedule with a joint variance prediction mechanism, accelerating the inference speed by an order of magnitude while maintaining high denoising quality. Furthermore, we integrate wavelet transforms into the encoder’s downsampling path, enabling adaptive feature fusion across frequency bands to enhance structural fidelity. Experimental results demonstrate that, compared to a baseline diffusion model, our proposed method achieves an approximately 20-fold acceleration in inference and obtains significant improvements in key objective metrics. This work contributes to real-time processing of diffusion models for SAR image enhancement, supporting practical deployment by mitigating prolonged inference in traditional diffusion models through efficient stochastic sampling. Full article

Internal waves (IWs) play a crucial role in the transport of energy and matter within the ocean while also posing significant risks to marine engineering, navigation, and underwater communication systems. Consequently, effective segmentation methods are essential for mitigating their adverse impacts and minimizing associated hazards. A promising strategy involves applying remote sensing image segmentation techniques to accurately identify IWs, thereby enabling predictions of their propagation velocity and direction. However, current IWs segmentation models struggle to balance computational efficiency and segmentation accuracy, often resulting in either excessive computational costs or inadequate performance. Motivated by recent developments in the Mamba2 architecture, this paper introduces the state-space model meets linear attention (SMLA), a novel segmentation framework specifically designed for IWs. The proposed hybrid architecture effectively integrates three key components: a feature-aware serialization (FAS) block to efficiently convert spatial features into sequences; a state-space model with linear attention (SSM-LA) block that synergizes a state-space model with linear attention for comprehensive feature extraction; and a decoder driven by hierarchical fusion and upsampling, which performs channel alignment and scale unification across multi-level features to ensure high-fidelity spatial detail recovery. Experiments conducted on a dataset of 484 synthetic-aperture radar (SAR) images containing IWs from the South China Sea achieved a mean Intersection over Union (MIoU) of 74.3%, surpassing competing methods evaluated on the same dataset. These results demonstrate the superior effectiveness of SMLA in extracting features of IWs from SAR imagery. Full article

Background: Acute SARS-CoV-2 infection may induce cardiac arrhythmias associated with viral myocarditis, which typically disappear in the convalescent phase after healing of the myocardial inflammation. Methods: We report the case of a 37-year-old woman with a childhood history of atrial septal defect repair and stable normofrequent atrial rhythm, who presented two months post-COVID-19 with palpitations and dizziness. Diagnostic evaluation included cardiac magnetic resonance imaging (CMR), 24 h Holter electrocardiogram (ECG) monitoring, and laboratory assessments over a 3-year period. Results: CMR suggested subacute myocarditis, and Holter ECG revealed multiple discernible complex cardiac arrhythmias including atrial bradycardia, intermittent junctional rhythm (JR), atrial fibrillation (AF), and non-sustained ventricular tachycardia. Laboratory results showed a moderate but transient increase in lactate dehydrogenase, persistently mildly elevated N-terminal pro–B-type natriuretic peptide (NT-proBNP), and immunoglobulin A (IgA), with all other cardiac, inflammatory, immunologic, and organ function parameters remaining normal. In spite of chaotic cardiac rhythm with alternating JR, AF, and atrial normofrequent rhythm with frequent blocked supraventricular beats and increasing atrioventricular conduction time, no therapeutic intervention was necessary during follow-up, and a conservative treatment approach was agreed with the patient. Two years post-COVID-19 infection, the patient returned to a normofrequent atrial rhythm with a markedly prolonged PQ time (500 ms) and a different P wave morphology compared to pre-COVID, without other rhythm disturbances. Conclusions: This case demonstrates a rare pattern of post-viral arrhythmias first emerging in the convalescent phase and resolving spontaneously after two years. It underscores the need for long-term rhythm surveillance following COVID-19, even in patients with prior structural heart disease and a stable baseline rhythm. Full article

Most electromagnetic invisibility devices are designed while relying on rigid structures, which have limitations in adapting to complex curved surfaces and dynamic deployment. In contrast, flexible invisibility structures have great application value due to their bendable and easy-to-fit characteristics. In this paper, we propose a flexible metasurface suitable for broadband SAR (Synthetic Aperture Radar) imaging invisibility, which realizes multi-domain joint regulation of electromagnetic waves by designing two subwavelength unit structures with differentiated reflection characteristics and combining array inverse optimization methods. The metasurface employs a sponge-like dielectric substrate and integrates resistive ink to construct a resonant structure, which can suppress electromagnetic scattering through joint phase and amplitude modulation, achieving low detectability of targets in UAV (Unmanned Aerial Vehicle) detection scenarios. Indoor microwave anechoic chamber tests and outdoor UAV-borne SAR experiments verify its stable invisibility performance in a wide frequency band, providing theoretical and experimental support for the application of flexible metasurfaces in dynamic electromagnetic detection countermeasures. Full article

Object detection in single synthetic aperture radar (SAR) imagery has always been essential for SAR interpretation. Over the years, the saliency-based detection method is considered as a strategy that can overcome some inherent deficiencies in traditional SAR detection and arouses widespread attention. Considering that the conventional saliency method usually suffers performance loss in saliency map generation from lacking specific task priors or highlighted non-object regions, this paper is devoted to achieving excellent salient object detection in single SAR imagery via a two-channel framework integrating Bayesian inference and adaptive iteration. Our algorithm firstly utilizes the two processing channels to calculate the object/background prior without specific task information and extract four typical features that can enhance the object presence, respectively. Then, these two channels are fused to generate an initial saliency map by Bayesian inference, in which object areas are assigned with high saliency values. After that, we develop an adaptive iteration mechanism to further modify the saliency map, during which object saliency is progressively enhanced while the background is continuously suppressed. Thus, in the final saliency map, there will be a distinct difference between object components and the background, allowing object detection to be realized easily by global threshold segmentation. Extensive experiments on real SAR images from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and SAR Ship Detection Dataset (SSDD) qualitatively and quantitatively demonstrate that our saliency map is superior to those of four classical benchmark methods, and final detection results of the proposed algorithm present better performance than several comparative methods across both ground and maritime scenarios. Full article

Pulmonary coinfection involving both viral and opportunistic pathogens is an emerging challenge in immunosuppressed patients. We report the case of a 59-year-old man with rheumatoid arthritis on long-term immunosuppressive therapy who developed necrotizing pneumonia and acute respiratory failure and was ultimately diagnosed with triple pulmonary coinfection by SARS-CoV-2, Nocardia cyriacigeorgica, and Aspergillus fumigatus. Diagnosis required comprehensive imaging, bronchoscopy with BAL, and microbiological work-up. The case was complicated by septic shock, multiple organ failure, and family-driven end-of-life decisions. This report highlights the diagnostic and therapeutic complexity of triple coinfection in the ICU, emphasizing the importance of systematic microbiology, imaging, and interdisciplinary care in critically ill immunocompromised hosts. Full article

The accurate estimation of building heights using very-high-resolution (VHR) synthetic aperture radar (SAR) imagery is crucial for various urban applications. This paper introduces a deep learning (DL)-based methodology for automated building height estimation from single VHR COSMO-SkyMed images: an object-based regression approach based on bounding box detection followed by height estimation. This model was trained and evaluated on a unique multi-continental dataset comprising eight geographically diverse cities across Europe, North and South America, and Asia, employing a cross-validation strategy to explicitly assess out-of-distribution (OOD) generalization. The results demonstrate highly promising performance, particularly on European cities where the model achieves a Mean Absolute Error (MAE) of approximately one building story (2.20 m in Munich), significantly outperforming recent state-of-the-art methods in similar OOD scenarios. Despite the increased variability observed when generalizing to cities in other continents, particularly in Asia with its distinct urban typologies and the prevalence of high-rise structures, this study underscores the significant potential of DL for robust cross-city and cross-continental transfer learning in building height estimation from single VHR SAR data. Full article

The upper Mapocho River basin, located in central Chile, has been affected by numerous landslides in the past, which may become more frequent due to a projected increase in intense precipitation events in the context of climate change. Against this background, this study aimed to analyze the ground deformation associated with an active landslide area in the Yerba Loca basin using the SBAS–DInSAR technique with PAZ and Sentinel-1 images acquired during two time periods, 2019–2021 and 2018–2022, respectively. Using PAZ imagery, the estimated vertical displacement velocity (subsidence) was as high as 9.6 mm/year between 2019 and 2021 in the area affected by the Yerba Loca multirotational slide in August 2018. Analysis of Sentinel-1 images indicated a vertical displacement velocity reaching −94 mm/year between 2018 and 2022 in the Yerba Loca landslide, suggesting continued activity in this area. It, therefore, may collapse again soon, affecting tourism services and the local ecosystem. By focusing on a mountainous region, this study demonstrates the usefulness of radar imagery for investigating landslides in remote or hard-to-reach areas, such as the mountain sector of central Chile. Full article

Background/Objectives: Intrinsic biomarkers, such as serum vitamin D levels and the conjunctival ultraviolet autofluorescence (CUVAF) area, have been proposed to quantify sunlight exposure. Evidence suggests that reduced outdoor activity during the SARS-CoV-2 pandemic accelerated the progression of myopia; however, there is little information on the impact of such restrictions on vitamin D levels and CUVAF area in populations with myopia. This study aims to assess the association between serum vitamin D levels and conjunctival ultraviolet autofluorescence area (CUVAF) in young adults with myopia during and after the pandemic, as well as its relationship with sun exposure habits and the use of skin protection measures. Methods: A prospective observational study was carried out. A total of 59 students participated, 32 with a diagnosis of myopia and 27 controls, during SARS-CoV-2 pandemic. Two serological tests for total 25-hydroxy vitamin D (D2 + D3) (Calciferol) were taken, activity habits and sun exposure were identified using the Intermountain Live Well Institute tool, and CUVAF images were taken post-pandemic. Results: In the 59 participants, we observed similar vitamin D concentrations between the myopic and control groups during and after the pandemic. However, analysis of CUVAF areas after the pandemic revealed that myopes had significantly smaller areas compared to controls (p < 0.05). Conclusions: The study demonstrated that using vitamin D as a biomarker for outdoor activity requires additional investigation; the CUVAF biomarker showed a significant association with myopia. Full article

Synthetic Aperture Radar (SAR) technology offers unparalleled advantages by delivering high-quality images under all-weather conditions, enabling effective flood monitoring. This capability provides massive remote sensing data for flood mapping, while recent rapid advances in deep learning (DL) offer methodologies for large-scale flood mapping. However, the full potential of deep learning in large-scale flood monitoring utilizing remote sensing data remains largely untapped, necessitating further exploration of both data and methodologies. This paper presents an innovative approach that harnesses convolutional neural networks (CNNs) with Sentinel-1 SAR images for large-scale inundation detection and dynamic flood monitoring in the Yangtze River Basin (YRB). An efficient CNN model entitled FloodsNet was constructed based on multi-scale feature extraction and reuse. The study compiled 16 flood events comprising 32 Sentinel-1 images for CNN training, validation, inundation detection, and flood mapping. A semi-automatic inundation detection approach was developed to generate representative flood samples with labels, resulting in a total of 5296 labeled flood samples. The proposed model FloodsNet achieves 1–2% higher F1-score than the other five DL models on this dataset. Experimental inundation detection in the YRB from 2016 to 2021 and dynamic flood monitoring in the Dongting and Poyang Lakes corroborated the scheme’s outstanding performance through various validation procedures. This study marks the first application of deep learning with SAR images for large-scale flood monitoring in the YRB, providing a valuable reference for future research in flood disaster studies. This study explores the potential of SAR imagery and deep learning in large-scale flood monitoring across the Yangtze River Basin, providing a valuable reference for future research in flood disaster studies. Full article

Digital Beamforming SAR (DBF-SAR) provides high-resolution wide-swath imaging capability, yet it is affected by inter-channel amplitude, phase and time-delay errors induced by temperature variations and random error factors. Since all elevation channel data are weighted and summed by the DBF module in real time, conventional record-then-compensate approaches cannot meet real-time processing requirements. To resolve the problem, a real-time calibration architecture for Intermediate Frequency DBF (IFDBF) is presented in this paper. The Field-Programmable Gate Array (FPGA) implementation estimates amplitude errors through simple summation, time-delay errors via a simple counter, and phase errors via single-bin Discrete-Time Fourier Transform (DTFT). The time-delay and phase error information are converted into single-tone frequency components through Dechirp processing. The proposed method deliberately employs a reduced-length DTFT implementation to achieve enhanced delay estimation range adaptability. The method completes calibration within tens of PRIs (under 1 s). The proposed method is analyzed and validated through a spaceborne simulation and X-band 16-channel DBF-SAR experiments. Full article

Recent advances in time-modulated metasurfaces (TMMs) have introduced approaches for controlling target features in radar imaging. These technologies enable dynamic reconstruction of scattering center locations and intensities by flexibly manipulating radar echoes. However, most existing methods focus on amplitude and phase modulation, lacking joint control over the polarimetric scattering characteristics of targets. As a result, the modulated outputs tend to exhibit limited polarimetric diversity and remain strongly tied to the targets’ physical structures. To address this limitation, this paper proposes a modulation method for polarimetric synthetic aperture radar (PolSAR) images based on a flexible metasurface with independently controllable polarizations (FM-ICP). The method independently controls the echo energy distribution in two polarization channels, enabling target representations in PolSAR images to exhibit polarimetric characteristics beyond their physical geometry—for example, rendering a flat plate as a cylinder, or vice versa. In addition, the method can generate synthetic scattering centers with controllable locations and polarimetric properties, which can be precisely tuned via modulation parameters. This work offers a practical approach for target feature manipulation and shows potential in PolSAR image simulation and feature reconstruction. Full article