Search Results (53)

Synthetic aperture radar (SAR) is an all-weather and all-day imaging technique for Earth observation. Achieving efficient observation, high resolution, and wide swath coverage have remained critical development goals in SAR technology, which inherently require extended synthetic aperture time. However, various non-ideal factors, including atmospheric disturbances, orbital perturbations, and antenna vibrations. degrade imaging performance, causing defocusing and ghost targets. Furthermore, the long synthetic time and large imaging swath further enlarge the temporal and spatial variability of these factors and seriously degrade the imaging effect. These inherent challenges make autofocusing indispensable for SAR imaging with a long synthetic time and large swath. In this paper, a novel autofocus method specifically designed to address these non-ideal factors is proposed for SAR imaging with a long synthetic time and large swath. The innovation of the method mainly consists of two parts. The first is the autofocus for multiple non-ideal factors, which is accomplished by an improved phase gradient autofocus (PGA) equipped with amplitude error estimation and discrete windowing. PGA with amplitude error estimation can solve the problem of defocus, and discrete windowing can focus the energy of paired echoes. The second is an error fusion and interpolation method for a long synthetic time and large swath. This method fuses errors among sub-apertures in the long synthetic time and can fulfill autofocus for blocks where strong scatterers are not sufficient in the large swath. The proposed method can effectively achieve SAR focusing with a long synthetic time and large swath, considering spatial and temporal variant non-ideal factors. Point target simulations and distributed target simulations based on real scenarios are conducted to validate the proposed method. Full article

This study focuses on step deformation landslides, conducting spatiotemporal analysis of landslide deformation and instability trends. First, the target landslide area is selected, and geological and precipitation data, along with historical displacement data from monitoring points, are collected. The slope single-change-point analysis method is then employed, combined with landslide profile data, to extract key features from the monitoring data. Next, Small BAseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology is applied to obtain satellite images of the study area. These images, together with the extracted data features, are used to draw the spatiotemporal baseline of the target landslide, completing the spatiotemporal analysis. Finally, a landslide prediction model is developed, and its prediction error is corrected using an Extreme Learning Machine (ELM) neural network. The refined prediction results serve as the basis for analyzing the landslide deformation coefficient, enabling the determination of the landslide instability trend. The experimental results show that step deformation landslides exhibit significant spatiotemporal variability and a short stability period throughout the year. The analytical methods designed in this study outperform traditional methods, providing more reliable results for predicting landslide instability trends. Full article

Long-span structures are highly vulnerable to wind-induced vibrations, which can pose a significant threat to their structural stability and safety. This paper introduces a novel monitoring method that combines Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology with Auto-Regressive Moving Average (ARMA) models, providing an innovative approach to monitoring wind-induced vibrations in large-span bridges. While previous studies have focused on individual techniques, this integrated approach is largely unexplored and offers a new perspective for structural health monitoring. By collating a series of SAR images and examining phase alterations on the bridge surface, a three-tiered detection methodology is employed to identify stable points accurately. The surface deformation data are then analyzed alongside wind speed and weather data to construct a comprehensive model elucidating the relationship between the bridge and vibrations. The ARMA model is used for real-time monitoring and assessment. Experimental results demonstrate that this method offers precise, real-time monitoring of wind-resistant stability. By leveraging the spatial accuracy and long-term monitoring capability of PS-InSAR, along with the time-series forecasting strength of ARMA models, the method enables data-driven analysis of bridge vibrations. It also provides comprehensive coverage under various conditions, enhancing the safety of long-span bridges through advanced predictive analytics. 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

Accurate near-surface wind speed and direction measurements are crucial for validating atmospheric models, especially for the purpose of adequately assessing the interactions between the surface and wind, which in turn results in characteristic vertical profiles. Coastal regions pose unique challenges due to the discontinuity between land and sea and the complex interplay of atmospheric stability, topography, and boundary/layer dynamics. This study focuses on a unique database of wind profiles collected over several years at a World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) coastal site in the southern Italian region of Calabria (Lamezia Terme, code: LMT). By leveraging remote sensing technologies, including wind lidar combined with in situ measurements, this work comprehensively analyzes wind circulation at low altitudes in the narrowest point of the entire Italian peninsula. Seasonal, daily, and hourly wind profiles at multiple heights are analyzed, highlighting the patterns and variations induced by land–sea interactions. A case study integrating Synthetic Aperture Radar (SAR) satellite images and in situ observations demonstrates the importance of multi-sensor approaches in capturing wind dynamics and validating model simulations. Data analyses demonstrate the occurrence of extreme events during the winter and spring seasons, linked to synoptic flows; fall seasons have variable patterns, while during the summer, low-speed winds and breeze regimes tend to prevail. The prevailing circulation is of a westerly nature, in accordance with other studies on large-scale flows. Full article

Ultrasonic TOFD imaging, as an important non-destructive testing method, has a wide range of applications in pipeline girth weld inspection and testing. Due to the limited bandwidth of ultrasonic transducers, near-surface defects in the weld are masked and cannot be recognized, resulting in poor longitudinal resolution. Affected by the inherent diffraction effect of scattered acoustic waves, defect images have noticeable trailing, resulting in poor transverse resolution of TOFD imaging and making quantitative defect detection difficult. In this paper, based on the assumption of the sparseness of ultrasonic defect distribution, by constructing a convolutional model of the ultrasonic TOFD signal, the Orthogonal Matching Pursuit (OMP) sparse deconvolution algorithm is utilized to enhance the longitudinal resolution. Based on the synthetic aperture acoustic imaging model, in the wavenumber domain, backpropagation inference is implemented through phase transfer technology to eliminate the influence of diffraction effects and enhance transverse resolution. On this basis, the time-domain sparse deconvolution and frequency-domain synthetic aperture focusing methods mentioned above are combined to enhance the resolution of ultrasonic TOFD imaging. The simulation and experimental results indicate that this technique can outline the shape of defects with fine detail and improve image resolution by about 35%. Full article

China’s first L-band fully polarimetric Synthetic Aperture Radar (SAR) constellation, LuTan-1 (LT-1), was designed for terrain mapping and geohazard monitoring. This study evaluates LT-1’s capability in identifying landslides in the southern hilly regions of China, focusing on Longshan County, Hunan Province. Using both ascending and descending orbit data from LT-1, we conducted landslide identification experiments. First, deformation was obtained using Differential Interferometric SAR (D-InSAR) technology, and the deformation rates were derived through the Stacking technique. A landslide identification method that integrates C-index, slope, and ascending/descending orbit deformation information was then applied. The identified landslides were validated against existing geohazard points and medium-to-high-risk slope and gully unit data. The experimental results indicate that LT-1-ascending orbit data identified 88 landslide areas, with 39.8% corresponding to geohazard points and 65.9% within known slope units. Descending orbit data identified 90 landslide areas, with 37.8% matching geohazard points and 61.1% within known slope units. The identification results demonstrated good consistency with existing data. Comparative analysis with Sentinel-1 data revealed that LT-1’s combined ascending and descending orbit data outperformed Sentinel-1’s single ascending orbit data. LT-1’s L-band characteristics, comprehensive ascending and descending orbit coverage, and high-precision deformation detection make it highly promising for landslide identification in the southern hilly regions. This study underscores LT-1’s robust technical support for early landslide identification, highlighting its potential to enhance geohazard monitoring and mitigate risks in challenging terrains. Full article

Satellite altimetry technology has been widely used for the observation of oceans and inland water bodies. At present, fully focused synthetic aperture radar (FF-SAR) data, which significantly enhances along-track resolution, has good prospects for river level estimation. However, FF-SAR data with large data volumes have more complex waveforms, which brings more challenges to waveform retracking. This study developed an improved adaptive multi-scale peak detection (ImpAMPD) retracker based on Sentinel-6 FF-SAR data. Initially, sub-waveforms are identified and extracted from each waveform. Subsequently, the data are segmented according to the number of gates and the minimum gate length. Finally, retracking calculations are performed on the segmented sub-waveforms to determine river levels. In this study, the in situ data from six river sections with different features in the middle and upper reaches of the Yangtze River were used to validate the accuracy of the ImpAMPD retracker and to perform a comparison of this developed retracker with three existing retrackers (OCOG, PTR, SAMOSA+). The results indicate that the ImpAMPD retracker can fully utilize the advantage of the high posting rate of FF-SAR data to process the complex multi-peak waveforms on the river surface, accurately extract the correct water surface signals, and achieve highly precise river level estimation. The best accuracy results were obtained in four river sections, namely, Zhicheng, Shashi, Hankou, and Huantan, with STDDs of 0.18 m, 0.26 m, 0.47 m, and 0.36 m, respectively. The ImpAMPD retracker is highly automated and adaptable to rivers of varying widths, providing robust support for river level monitoring and flood management. Full article

Long synthetic aperture radar (SAR) offers the advantage of achieving higher resolution by utilizing longer synthetic aperture times, which makes it a promising technology for ocean observation in the future. However, compared to SAR systems with shorter synthetic aperture times, it suffers more severely from issues such as image defocusing, blurring and artifacts during the observation of maritime targets, due to motion errors. To improve the quality of SAR imaging against motion errors in long synthetic aperture time scenarios, this paper proposes a combined motion compensation (MOCO) method based on subaperture processing. The method first divides the full aperture data into several subapertures. Within each subaperture, the platform is assumed to move at approximately constant velocity. The major imaging step is then combined with two motion compensation operations, which are performed individually within each subaperture. Then, the processed subaperture data are stitched together, and finally, the residual errors are compensated by the third MOCO, resulting in the final image. By simulating maritime observation targets with point targets, simulation results demonstrate that the proposed MOCO algorithm effectively reduce the influence of motion errors, suppress the sidelobe interference to the imaging, and improve the focusing accuracy. Compared with other classical MOCO algorithms, the ISLR_r and ISLR_a metrics show improvements of 0.2662 and 0.8170 dB, respectively. Further verification of the proposed method is conducted by processing the imaging results of measured sea surface data. The proposed algorithm produces clearer wave textures and achieves better imaging performance on targets such as ships in the sea. This result validates the effectiveness and superiority of the proposed method. The proposed method effectively addresses the need for high-precision motion error compensation in high-resolution imaging within long synthetic aperture time system. Full article

Radio frequency (RF) spectrum sensing is critical for applications requiring precise object and posture detection and classification. This survey aims to provide a focused review of context-aware RF-based sensing, emphasizing its principles, advancements, and challenges. It specifically examines state-of-the-art techniques such as phased array radar, synthetic aperture radar, and passive RF sensing, highlighting their methodologies, data input domains, and spatial diversity strategies. The paper evaluates feature extraction methods and machine learning approaches used for detection and classification, presenting their accuracy metrics across various applications. Additionally, it investigates the integration of RF sensing with other modalities, such as inertial sensors, to enhance context awareness and improve performance. Challenges like environmental interference, scalability, and regulatory constraints are addressed, with insights into real-world mitigation strategies. The survey concludes by identifying emerging trends, practical applications, and future directions for advancing RF sensing technologies. Full article

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives. Full article

The paper presents a GIS plugin, named Bridge Assessment System via MTInSAR (BAS-MTInSAR), aimed at assessing deformations in existing simply supported concrete girder bridges through Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). Existing bridges require continuous maintenance to ensure functionality toward external effects undermining the safety of these structures, such as aging, material degradation, and environmental factors. Although effective and standardized methodologies exist (e.g., structural monitoring, periodic onsite inspections), new emerging technologies could be employed to provide time- and cost-effective information on the current state of structures and to drive prompt interventions to mitigate risk. One example is represented by MTInSAR data, which can provide near-continuous information about structural displacements over time. To easily manage these data, the paper presents BAS-MTInSAR. The tool allows users to insert information of the focused bridge (displacement time series, structural information, temperature data) and, through a user-friendly GUI, observe the occurrence of abnormal deformations. In addition, the tool implements a procedure of multisource data management and defines proper thresholds to assess bridge behavior against current code prescriptions. BAS-MTInSAR is fully described throughout the text and was tested on a real case study, showing the main potentialities of the tool in managing bridge portfolios. Full article

With the continuous development of synthetic aperture radar (SAR) jamming technology, low-power maneuvering repeater jammers are now flexible and can be located on multiple unmanned aerial vehicles (UAVs) and unmanned vehicles (UVs) at the same time, which greatly increases the difficulty of the anti-maneuvering repeater jamming method for spaceborne SAR. Due to the low-power transmission, the locations of the low-power repeater jammers and the protected areas in the imaged swath are relatively close in distance, while the transmission delay of the jamming is approximately equal to the pulse repetition interval (PRI). According to this phenomenon, an anti-maneuvering repeater jamming method using up- and down-chirp modulation is proposed in this paper. After alternately transmitting up- and down-chirp modulation signals, echoes of the jamming and the protected area are recorded in the same location within the echo-receiving window and are related to different chirp modulations. To remove the jamming echoes, de-chirping and frequency filtering are adopted after echo data segmentation. With jamming interference removal using frequency notch filtering, parts of the spectra corresponding to the desired echoes of the imaged swath are simultaneously removed. To recover the unwanted removed range spectra, linear prediction is introduced to improve the focusing quality. Finally, simulation results on both point and distributed targets validate the proposed anti-maneuvering repeater jamming method by using up- and down-chirp modulation. Full article

Flint Hall Farm in Godstone, Surrey, UK, is situated adjacent to the London Orbital Motorway, or M25, and contains several landslide systems which pose a significant geohazard risk to this critical infrastructure. The site has been routinely monitored by geotechnical engineers following a landslide that encroached onto the hard shoulder in December 2000; current in situ instrumentation includes inclinometers and piezoelectric sensors. Interferometric Synthetic Aperture Radar (InSAR) is an active remote sensing technique that can quantify millimetric rates of Earth surface and structural deformation, typically utilising satellite data, and is ideal for monitoring landslide movements. We have developed the hardware and software for an Unmanned Aerial Vehicle (UAV), or drone radar system, for improved operational flexibility and spatial–temporal resolutions in the InSAR data. The hardware payload includes an industrial-grade DJI drone, a high-performance Ettus Software Defined Radar (SDR), and custom Copper Clad Laminate (CCL) radar horn antennas. The software utilises Frequency Modulated Continuous Wave (FMCW) radar at 5.4 GHz for raw data collection and a Range Migration Algorithm (RMA) for focusing the data into a Single Look Complex (SLC) Synthetic Aperture Radar (SAR) image. We present the first SAR image acquired using the drone radar system at Flint Hall Farm, which provides an improved spatial resolution compared to satellite SAR. Discrete targets on the landslide slope, such as corner reflectors and the in situ instrumentation, are visible as bright pixels, with their size and positioning as expected; the surrounding grass and vegetation appear as natural speckles. Drone SAR imaging is an emerging field of research, given the necessary and recent technological advancements in drones and SDR processing power; as such, this is a novel achievement, with few authors demonstrating similar systems. Ongoing and future work includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslides and other geotechnical hazards and infrastructure. Full article

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method. Full article