Search Results (351)

The city of Acapulco was impacted by two near-consecutive hurricanes. On 25 October 2023, Hurricane Otis made landfall, reaching the highest Category 5 storm on the Saffir–Simpson scale, causing extensive coastal destruction due to extreme winds and waves. Nearly one year later (23 September 2024), Hurricane John—a Category 2 storm—caused severe flooding despite its lower intensity, primarily due to its unusual trajectory and prolonged rainfall. Digital shoreline analysis of PlanetScope images (captured one month before and after Hurricane Otis) revealed that the southern coast of Acapulco, specifically Zona Diamante—where the major seafront hotels are located—experienced substantial shoreline erosion (94 ha) and damage. In the northwestern section of the study area, the Coyuca Bar experienced the most dramatic geomorphological change in surface area. This was primarily due to the complete disappearance of the bar on October 26, which resulted in a shoreline retreat of 85 m immediately after the passage of Hurricane Otis. Sentinel-1 Synthetic Aperture Radar (SAR) showed that Hurricane John inundated 2385 ha, four times greater than Hurricane Otis’s flooding (567 ha). The retrofitted QGIS methodology demonstrated high reliability when compared to limited in situ local reports. Given the increased frequency of intense hurricanes, these methods and findings will be relevant in other coastal areas for monitoring and managing local communities affected by severe climate events. Full article

Synthetic Aperture Radar (SAR) images and optical imagery have high potential for extracting digital elevation models (DEMs). The two main approaches for deriving elevation models from SAR data are interferometry (InSAR) and radargrammetry. Adapted from photogrammetric principles, radargrammetry relies on disparity model estimation as its core component. Matching strategies in radargrammetry typically follow local, global, or semi-global methodologies. Local methods, while having higher accuracy, especially in low-texture SAR images, require larger kernel sizes, leading to quadratic computational complexity. Conversely, global and semi-global models produce more consistent and higher-quality disparity maps but are computationally more intensive than local methods with small kernels and require more memory (RAM). In this study, inspired by the advantages of local matching algorithms, a computationally efficient and novel model is proposed for extracting corresponding pixels in SAR-intensity stereo images. To enhance accuracy, the proposed two-stage algorithm operates without an image pyramid structure. Notably, unlike traditional local and global models, the computational complexity of the proposed approach remains stable as the input size or kernel dimensions increase while memory consumption stays low. Compared to a pyramid-based local normalized cross-correlation (NCC) algorithm and adaptive semi-global matching (SGM) models, the proposed method maintains good accuracy comparable to adaptive SGM while reducing processing time by up to 50% relative to pyramid SGM and achieving a 35-fold speedup over the local NCC algorithm with an optimal kernel size. Validated on a Sentinel-1 stereo pair with a 10 m ground-pixel size, the proposed algorithm yields a DEM with an average accuracy of 34.1 m. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

Visual navigation technology holds significant potential for applications involving unmanned aerial vehicles (UAVs). However, the inherent spectral limitations of optical-dependent navigation systems prove particularly inadequate for high-altitude long-endurance (HALE) UAV operations, as they are fundamentally constrained in maintaining reliable environment perception under conditions of fluctuating illumination and persistent cloud cover. To address this challenge, this paper introduces microwave vision to assist optical vision for environmental measurement and proposes a novel microwave vision-enhanced environmental perception method. In particular, the richness of perceived environmental information can be enhanced by SAR and optical image fusion processing in the case of sufficient light and clear weather. In order to simultaneously mitigate inherent SAR speckle noise and address existing fusion algorithms’ inadequate consideration of UAV navigation-specific environmental perception requirements, this paper designs a SAR Target-Augmented Fusion (STAF) algorithm based on the target detection of SAR images. On the basis of image preprocessing, this algorithm utilizes constant false alarm rate (CFAR) detection along with morphological operations to extract critical target information from SAR images. Subsequently, the intensity–hue–saturation (IHS) transform is employed to integrate this extracted information into the optical image. The experimental results show that the proposed microwave vision-enhanced environmental perception method effectively utilizes microwave vision to shape target information perception in the electromagnetic spectrum and enhance the information content of environmental measurement results. The unique information extracted by the STAF algorithm from SAR images can effectively enhance the optical images while retaining their main attributes. This method can effectively enhance the environmental measurement robustness and information acquisition ability of the visual navigation system. Full article

Accurate and timely flood mapping is critical for informing emergency response and risk mitigation during extreme weather events. This study presents a synthetic aperture radar (SAR)-based approach for rapid flood extent mapping using Sentinel-1 imagery, demonstrated for Tropical Storm Imelda (17–21 September 2019) in southeastern Texas. Dual-polarization Sentinel-1 SAR data (VH and VV) were processed by computing the VH/VV backscatter ratio, and the resulting ratio image was classified using a supervised Random Forest classifier to delineate water and land. All Sentinel-1 images underwent radiometric calibration, speckle noise filtering, and terrain correction to ensure precision in flood delineation. The Random Forest classifier achieved an overall flood mapping accuracy exceeding 94%, with Cohen’s kappa coefficients of approximately 0.75–0.80, demonstrating the approach’s reliability in distinguishing transient floodwaters from permanent water bodies. The spatial distribution of flooding was strongly influenced by topography and land cover. Analysis of Shuttle Radar Topography Mission (SRTM) digital elevation data revealed that low-lying, flat terrain was most vulnerable to inundation; correspondingly, the land cover types most affected were hay/pasture, cultivated land, and emergent wetlands. Additionally, urban areas with low-intensity development experienced extensive flooding, attributed to impervious surfaces exacerbating runoff. A strong, statistically significant correlation (R² = 0.87, p < 0.01) was observed between precipitation and flood extent, indicating that heavier rainfall led to greater inundation; accordingly, the areas with the highest rainfall totals (e.g., Jefferson and Chambers counties) experienced the most extensive flooding, as confirmed by SAR-based change detection. The proposed approach eliminates the need for manual threshold selection, thereby reducing misclassification errors due to speckle noise and land cover heterogeneity. Harnessing globally available Sentinel-1 data with near-real-time processing and a robust classifier, this approach provides a scalable solution for rapid flood monitoring. These findings underscore the potential of SAR-based flood mapping under adverse weather conditions, thereby contributing to improved disaster preparedness and resilience in flood-prone regions. Full article

Coal mines play an important role in the global energy supply. Monitoring the displacement of open-pit mines is crucial to preventing geological disasters, such as landslides and surface displacement, caused by high-intensity mining activities. In recent years, multi-temporal Synthetic Aperture Radar Interferometry (InSAR) technology has advanced and become widely used for monitoring the displacement of open-pit mines. However, the scattering characteristics of surfaces in open-pit mining areas are unstable, resulting in few coherence points with uneven distribution. Small BAseline Subset InSAR (SABS-InSAR) technology struggles to extract high-density points and fails to capture the overall displacement trend of the monitoring area. To address these challenges, this study focused on the Shengli West No. 2 open-pit coal mine in eastern Inner Mongolia, China, using 201 Sentinel-1 images collected from 20 May 2017 to 13 April 2024. We applied both SBAS-InSAR and distributed scatterer InSAR (DS-InSAR) methods to investigate the surface displacement and long-term behavior of the open-pit coal mine over the past seven years. The relationship between this displacement and mining activities was analyzed. The results indicate significant land subsidence was observed in reclaimed areas, with rates exceeding 281.2 mm/y. The compaction process of waste materials was the main contributor to land subsidence. Land uplift or horizontal displacement was observed over the areas near the active working parts of the mines. Compared to SBAS-InSAR, DS-InSAR was shown to more effectively capture the spatiotemporal distribution of surface displacement in open-pit coal mines, offering more intuitive, comprehensive, and high-precision monitoring of open-pit coal mines. Full article

Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning. Full article

The novel coronavirus pandemic, SARS-CoV-2, has a variable clinical spectrum, ranging from asymptomatic to critical forms. High mortality and morbidity rates have been associated with risk factors such as comorbidities, age, sex, and virulence factors specific to viral variants. Material and Methods: We retrospectively evaluated imaging characteristics using the Brixia radiological score in relation to favorable or unfavorable outcomes in adult patients. We included COVID-19 cases, admitted between 2020 and 2022, in a specialized pulmonology hospital with no intensive care unit. We analyzed 380 virologically confirmed COVID-19 cases, with a mean age of 52.8 ± 13.02 years. The mean Brixia radiological score at admission was 5.13 ± 3.56, reflecting predominantly mild-to-moderate pulmonary involvement. Multivariate analysis highlighted the utility of this score as a predictive marker for COVID-19 prognosis, with values >5 correlating with other severity biomarkers, NEWS-2 scores, and a lack of vaccination and hospitalization delay of more than 6 days from symptom onset. Summarizing, the Brixia score is itself an effective tool for screening COVID-19 cases at risk of death for early recognition of clinical deterioration and for decisions regarding appropriate care settings. Promoting vaccination can reduce the severity of radiological lesions, thereby decreasing the risk of death. Technologies based on artificial intelligence could optimize diagnosis and management decisions. Full article

Background/Objectives: Deep inferior epigastric perforator (DIEP) flap reconstruction is considered the gold standard for autologous breast reconstruction due to its favorable aesthetic results and low donor site morbidity. Nevertheless, it remains associated with potentially life-threatening complications such as deep vein thrombosis (DVT) and pulmonary embolism (PE). This report aims to describe a complex clinical case in which severe thromboembolic and ischemic complications occurred despite adherence to standard prophylactic protocols. Methods: We present the case of a 65-year-old female with multiple thromboembolic risk factors—including obesity, a history of heavy smoking, hormone therapy, and prior COVID-19 infection—who underwent immediate bilateral breast reconstruction with DIEP flaps following mastectomy. Results: Within the first 24 h postoperatively, the patient developed a massive pulmonary embolism requiring intensive care management. Despite appropriate anticoagulation and supportive measures, she subsequently experienced full-thickness necrosis of the central portion of the abdominal flap. Thrombophilia screening and diagnostic imaging did not reveal peripheral venous thrombosis, raising the hypothesis of a hypercoagulable state potentially related to prior SARS-CoV-2 infection. Conclusions: This case underscores the importance of individualized risk stratification and suggests that current prophylaxis protocols may be insufficient for patients with overlapping thrombotic risk factors. The findings advocate for further investigation into the long-term vascular effects of COVID-19 and support reconsidering extended or intensified prophylaxis in high-risk populations undergoing complex microsurgical procedures. Full article

In the process of mosaicking regional synthetic aperture radar (SAR) intensity images, multiple images with significant brightness anomalies can cause a considerable number of pixels to exceed the grayscale quantization range. Applying traditional color harmonization methods increases this issue, causing a loss of brightness information. We propose a multi-objective gray consistency correction method designed explicitly for mosaicking regional SAR intensity images with brightness anomalies to address this. We constructed a two-objective optimization model to ensure regional image gray consistency and mitigate brightness information loss. The truncation values of brightness anomaly images were selected as decision variables, maximizing the overall gray consistency of overlapping image pairs and minimizing the number of pixels with grayscale values that were out of bounds as the objective functions. To synchronously solve the truncation values of brightness anomaly images and linear stretch parameters of all images, a hybrid framework that combines the non-dominated sorting genetic algorithm II (NSGA-II) with the quadratic programming (QP) algorithm was proposed. Two large-area experimental results show that the proposed method achieves a balanced optimization between gray consistency and brightness information loss for regional SAR intensity image mosaicking. Compared with the traditional method, our method reduces brightness information loss by 99.552–99.647% and 99.973–99.969%, respectively, while maintaining better peak signal-to-noise ratio performance. Full article

This paper presents a backprojection algorithm (BPA) accelerator implemented on a field-programmable gate array (FPGA) for circular synthetic aperture radar (SAR) systems. Although the BPA offers superior image quality, it requires significantly more computation and is memory intensive, necessitating hardware optimization. In particular, the BPA accumulates image data, leading to high memory requirements that must be reduced for embedded system implementation. To address this issue, we optimized the floating-point (FP) bit width, focusing on the output data that form the image, rather than only reducing the internal computation bit widths as in previous studies. Specifically, we optimized the exponent and mantissa widths in six computational units, prioritizing memory optimization for image data before reducing the computational logic. The proposed BPA accelerator achieved a 77% reduction in memory usage and a 73–74% reduction in computational logic while maintaining an image quality with a structural similarity index measure (SSIM) of 0.99 or higher. These optimizations significantly enhanced the feasibility of BPA processing in embedded systems. Full article

Spaceborne Synthetic Aperture Radar (SAR) is extensively used in maritime surveillance due to its ability to monitor vast oceanic regions regardless of weather conditions and sun illumination. Over the years, numerous automatic ship detection algorithms have been developed, utilizing either single-polarimetric data (i.e., intensity) or leveraging additional information provided by polarimetric sensors. One of the main challenges in automatic ship detection using SAR is that sea clutter, influenced primarily by sea conditions and image acquisition angles, can exhibit strong backscatter, reducing the signal-to-clutter ratio (that is, the contrast) between ships and their surroundings. This leads inevitably to detection errors, which can be either false alarms or miss-detections. A potential solution to this issue is to develop methodologies that suppress backscattered signals from the sea while preserving the radar returns from ships. In this work, we analyse a contrast enhancement method which is designed to suppress unwanted sea clutter while preserving signals from potential ships. A key advantage of this method is that it is fully analytical, eliminating the need for numerical optimization and enabling the rapid generation of an enhanced image better suited for automatic detection. This technique, based on polarimetric orthogonality, was originally formulated for quad-polarimetric data, and here the adaptation for dual-polarimetric SAR images is also detailed. To demonstrate its effectiveness, a comprehensive set of results using both quad- and dual-polarimetric images acquired by various sensors operating at L-, C-, and X-band is presented. Full article

The prerequisite for the fusion processing of heterogeneous SAR images lies in high-precision image matching, which can be widely applied in areas such as geometric localization, scene matching navigation, and target recognition. This study proposes a method for high-precision matching of heterogeneous SAR images based on the combination of the single-scale ratio of an exponentially weighted averages (ROEWA) operator and angle-weighted gradient (RAWG). The method consists of the following three main steps: feature point extraction, feature description, and feature matching. The algorithm utilizes the block-based SAR-Harris operator to extract feature points from the reference SAR image, effectively combating the interference of coherent speckle noise and improving the uniformity of feature point distribution. By employing the single-scale ROEWA operator in conjunction with angle-weighted gradient projection, the construction of a 3D dense feature descriptor is achieved, enhancing the consistency of gradient features in heterogeneous SAR images and smoothing the search surface. Through the optimal feature construction strategy and frequency domain SSD algorithm, fast template matching is realized. Experimental comparisons with other mainstream matching methods demonstrate that the Root Mean Square Error (RMSE) of our method is reduced by 47.5% compared with CFOG, and compared with HOPES, the error is reduced by 15.4% and the matching time is reduced by 34.3%. The proposed approach effectively addresses the nonlinear intensity differences, geometric disparities, and interference of coherent speckle noise in heterogeneous SAR images. It exhibits robustness, high precision, and efficiency as its prominent advantages. Full article

Tomographic synthetic aperture radar (TomoSAR) is an advanced three-dimensional (3D) synthetic aperture radar (SAR) imaging technology that can obtain multiple SAR images through multi-track observations, thereby reconstructing the 3D spatial structure of targets. However, due to system limitations, the multi-baseline (MB) monostatic SAR (MonoSAR) encounters temporal decorrelation issues when observing the scene such as forests, affecting the accuracy of the 3D reconstruction. Additionally, during TomoSAR observations, the platform jitter and inaccurate position measurement will contaminate the MB SAR data, which may result in the multiplicative noise with phase errors, thereby leading to the decrease in the imaging quality. To address the above issues, this paper proposes a MB bistatic SAR (BiSAR) 3D imaging method based on the phase error calibration that combines the phase gradient autofocus (PGA) and energy balance intensity-squared optimization autofocus (EB-ISOA). Firstly, the signal model of the MB one-stationary (OS) BiSAR is established and the 3D imaging principle is presented, and then the phase error caused by platform jitter and inaccurate position measurement is analyzed. Moreover, combining the PGA and EB-ISOA methods, a 3D imaging method based on the phase error calibration is proposed. This method can improve the accuracy of phase error calibration, avoid the vertical displacement, and has the noise robustness, which can obtain the high-precision 3D BiSAR imaging results. The experimental results are shown to verify the effectiveness and practicality of the proposed MB BiSAR 3D imaging method. Full article

Due to the limited availability of high-quality optical images during the rice growth period in the Lingnan region of China, effectively monitoring the rice planting situation has been a challenge. In this study, we utilized multi-temporal Sentinel-1 data to develop a method for rapidly extracting the range of rice fields using a threshold segmentation approach and employed a U-Net deep learning model to delineate the distribution of rice fields. Spatio-temporal changes in rice distribution in Leizhou City, Guangdong Province, China, from 2017 to 2021 were analyzed. The results revealed that by analyzing SAR-intensive time series data, we were able to determine the backscattering coefficient of typical crops in Leizhou and use the threshold segmentation method to identify rice labels in SAR-intensive time series images. Furthermore, we extracted the distribution range of early and late rice in Leizhou City from 2017 to 2021 using a U-Net model with a minimum relative error accuracy of 3.56%. Our analysis indicated an increasing trend in both overall rice planting area and early rice planting area, accounting for 44.74% of early rice and over 50% of late rice planting area in 2021. Double-cropping rice cultivation was predominantly concentrated in the Nandu River basin, while single-cropping areas were primarily distributed along rivers and irrigation facilities. Examination of the traditional double-cropping areas in Fucheng Town from 2017 to 2021 demonstrated that over 86.94% had at least one instance of double cropping while more than 74% had at least four instances, which suggested that there is high continuity and stability within the pattern of rice cultivation practices observed throughout Leizhou City. Full article