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Estuarine intertidal flats are exposed to a wide range of changing environmental conditions, but despite many years of field studies, little is known about their geotechnical characteristics. This study investigates (1) the variability of sediment grain size distributions, water contents, and Atterberg limits of three distinct exposed tidal flats; and (2) the potential pathways for remote characterization of those sites. Sediment samples were collected from three flats in the Great Bay Estuary located in the Gulf of Maine at the border of New Hampshire and Maine, United States: two sites composed of primarily fine-grained soils (Adams Point and Woody) and one composed of predominately coarse-grained soils (Mast Cove). Additionally, high-resolution X-band synthetic aperture radar (SAR) images were collected concurrently to field measurements from various satellites and over different incidence angles. In each image, the footprint area corresponding to the three exposed tidal flats was extracted, and statistical properties of the backscatter intensity histograms for each region were computed and compared. Moisture contents, fines contents, and grain size distributions varied between the three sites, with the USCS classifications ranging from elastic silts (Woody) and low-plasticity silts (Adams Point) with high water contents (83.2%–172.7%) and fines contents (83.4%–99.5%) to silty sand and well-graded sands with gravels (Mast Cove) with lower water contents (17.7%–48.3%) and fines contents (4.8%–44.3%). Across the sites, the statistical properties of the SAR backscatter exhibited distinct trends regarding mean backscatter, entropy, and uniformity in relation to the Unified Soil Classification Scheme (USCS) classification and fines content. These trends were consistent across all images collected. A potential sediment classification scheme to derive the USCS classification from the X-band SAR imagery is suggested utilizing the mean, entropy, and uniformity of the backscatter coefficient. [ABSTRACT FROM AUTHOR]

Both unresolved physics in numerical models and limited theoretical understanding of the small-scale diffusion processes occurring near the ocean surface hamper predictability of tropical cyclone (TC) wind changes. An analytical model is here developed to diagnose the short-term evolution of the TC wind profile. An effective frictional parameter is introduced to control the unknown diffusion effects. When this frictional parameter is adjusted to match the TC intensity change, solutions are consistent with observed high-resolution ocean surface wind speeds from satellite synthetic aperture radar (SAR). The initial high-resolution estimate of the near-core wind structure is then found to strongly modulate the wind profile evolution. The frictional parameter can, unfortunately, not efficiently be calibrated using outer-core wind speed changes. Low-resolution observations or standard numerical weather predictions may thus not be directly used to reinterpret and anticipate short-term TC wind changes. The expected accumulation of orbiting SAR sensors as well as improved measurements of the ocean-atmosphere boundary layer characteristics shall then become essential to more precisely monitor TC dynamics. [ABSTRACT FROM AUTHOR]

The unknown relative motions between synthetic aperture radar (SAR) and a ground moving target will lead to serious range cell migration (RCM) and Doppler frequency spread (DFS). The energy of the moving target will defocus, given the effect of the RCM and DFS. The moving target will easily produce Doppler ambiguity, due to the low pulse repetition frequency of radar, and the Doppler ambiguity complicates the corrections of the RCM and DFS. In order to address these issues, an efficient ground moving target focusing method for SAR based on scaled Fourier transform and scaled inverse Fourier transform is presented. Firstly, the operations based on the scaled Fourier transform and scaled inverse Fourier transforms are presented to focus the moving targets in consideration of Doppler ambiguity. Subsequently, in accordance with the detailed analysis of multiple target focusing, the spurious peak related to the cross term is removed. The proposed method can accurately eliminate the DFS and RCM, and the well-focused result of the moving target can be achieved under the complex Doppler ambiguity. Then, the blind speed sidelobe can be further avoided. The presented method has high computational efficiency without the step of parameter search. The simulated and measured SAR data are provided to demonstrate the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

For synthetic aperture microwave radiometers, the problem of Radio Frequency Interference (RFI) is becoming more and more serious, which affects both the scientific retrieval of remote sensing data and the imaging quality of brightness temperature (BT) images. In the visibility data domain, the array factor synthesis algorithm is commonly employed to mitigate RFI sources and their Gibbs trailing. In the BT image domain, the CLEAN algorithm is typical applied to mitigate RFI sources and their Gibbs trailing. However, the array factor synthesis algorithm can result in anomalous BT points near the "zero trap" region, and the CLEAN algorithm will miss some BT points below a certain threshold. In this paper, a Bi-domain combined mitigation algorithm is proposed to mitigate RFI sources and their Gibbs trailing. Following initial mitigation in the visibility data domain, dual thresholds are applied to normalize anomalous BT points near the "zero trap" region, thereby enhancing imaging quality. The effectiveness of the Bi-domain combined mitigation algorithm is verified by using both measured data from SMOS L1A and simulated data. The experimental results demonstrate that the Bi-domain combined mitigation algorithm is superior to the array factor synthesis algorithm and the CLEAN algorithm in mitigating RFI sources and their Gibbs trailing. [ABSTRACT FROM AUTHOR]

The significant differences in data domains between SAR images and the expensive and time-consuming process of data labeling pose significant challenges to terrain classification. Current terrain classification methodologies face challenges in addressing domain disparities and detecting uncommon terrain effectively. Based on Style Transformation and Domain Metrics (STDMs), we propose an unsupervised domain adaptive framework named STDM-UDA for terrain classification in this paper, which consists of two steps: image style transfer and domain adaptive segmentation. As a first step, image style transfer is performed within the image space to mitigate the differences in low-level features between SAR image domains. Subsequently, leveraging this process, the segmentation network extracts image features, employing domain metrics and adversarial training to enhance alignment between domain gaps in the semantic feature space. Finally, experiments conducted on several pairs of SAR images, each exhibiting varying degrees of differences in key imaging parameters such as source, resolution, band, and polarization, demonstrate the robustness of the proposed method. It achieves remarkably competitive classification accuracy, particularly for unlabeled, high-resolution broad scenes, effectively overcoming the domain gaps introduced by the diverse imaging parameters under studies. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection. [ABSTRACT FROM AUTHOR]

The utilization of optical and synthetic aperture radar (SAR) multi-source data to obtain better land classification results has received increasing research attention. However, there is a large property and distributional difference between optical and SAR data, resulting in an enormous challenge to fuse the inherent correlation information to better characterize land features. Additionally, scale differences in various features in remote sensing images also influence the classification results. To this end, an optical and SAR Siamese semantic segmentation network, OPT-SAR-MS2Net, is proposed. This network can intelligently learn effective multi-source features and realize end-to-end interpretation of multi-source data. Firstly, the Siamese network is used to extract features from optical and SAR images in different channels. In order to fuse the complementary information, the multi-source feature fusion module fuses the cross-modal heterogeneous remote sensing information from both high and low levels. To adapt to the multi-scale features of the land object, the multi-scale feature-sensing module generates multiple information perception fields. This enhances the network's capability to learn contextual information. The experimental results obtained using WHU-OPT-SAR demonstrate that our method outperforms the state of the art, with an mIoU of 45.2% and an OA of 84.3%. These values are 2.3% and 2.6% better than those achieved by the most recent method, MCANet, respectively. [ABSTRACT FROM AUTHOR]

Drone-based synthetic aperture radar (SAR) analysis offers a simple and efficient technique for acquiring observational data from various research areas. However, fundamental research on observation techniques, including the investigation of drone flight parameters, remains insufficient. This study focuses on improving the resolution of intensity images of a single-look complex (SLC) via drone-based SAR observations. By conducting experiments at different flight speeds and analysing the obtained SLC images, we demonstrated that higher drone speeds resulted in enhanced image resolution under the conditions investigated at flight speeds of ≤ 5 m s−1. A comparison with satellite SAR images highlighted the superior performance of drone-based SAR in capturing topographic features. The analysis also suggested that factors including trajectory deviation and Doppler rate affected the resolution and azimuth position. The simulation results supported the experimental findings on the relationship between drone speed and resolution. Our results emphasize the potential of drone-based SAR observations for monitoring slope failures and suggest the importance of the drone flight parameter selection. In the future, drone-based SAR is expected to produce intensity images of slopes and locate slope deformations associated with blasting for mining and earthquakes. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) images have been widely used for surface water identification due to their all-weather capabilities. However, the presence of inherent speckle noise in SAR data poses a challenge for accurate water identification. Additionally, annotating high-quality water body samples requires significant human labour, which can be costly and time-consuming. Aiming at the above problems, a noise-robust automatic water identification architecture without artificial labels is proposed. First, a two-stage automatic sample collection method that utilizes k-means++ clustering and morphological concepts is designed. Then, a weakly supervised noise-resistant SAR water body segmentation method NRM-ACUNet has been developed based on U-Net combined with LNR-Dice loss function and Conditionally Parameterized Convolutions (CondConv) to minimize the impact of sample noises. Experimental results show that the morphological processing can improve water body sample quality compared to k-means++, and compared with U-Net, NRM-ACUNet performs superior with noise-containing pseudo-samples, achieving 96.8% F1 accuracy and 52.06% accuracy improvement. [ABSTRACT FROM AUTHOR]

Launched on 2022 December 16, the Surface Water and Ocean Topography (SWOT) satellite, using synthetic aperture radar interferometric techniques, measures sea surface heights (SSHs) across two 50-km-wide swaths, offering high-resolution and accurate 2-D SSH observations. This study explores the efficiency of SWOT in seamount detection employing the vertical gravity gradient (VGG) derived from simulated SWOT SSH data. Simulated circular and elliptical seamounts (height: 900–1500 m) are integrated within the South China Sea's 4000 m background depths. Geoid perturbations induced by these seamounts are extracted through the residual depth model principle, subsequently merged with the DTU21MSS model for simulating SWOT SSH observations. For comparative assessment, SSH data from Jason-2 and Cryosat-2 are included. An automatic algorithm (AIFS) is presented to identify seamount centres and base polygons using VGG derived from simulated altimeter SSH data. The analysis reveals SWOT-derived VGGs precisely locate all seamount centres, base polygons and elliptical seamount azimuths. The merged Jason-2 and Cryosat-2 data face challenges with identifying small circular and elliptical seamounts. Detecting long narrow elliptical seamounts remains arduous; however, SWOT-derived VGGs successfully elucidate the approximate shapes and major axis azimuths of the elliptical seamounts. Validated against 'true values' of VGG, the root-mean-squared deviation (RMSD) of SWOT-derived VGG stands at 1.33 Eötvös, whereas the merged Jason-2 and Cryosat-2 data exhibit an RMSD of 1.93 Eötvös. This study shows the enhanced capability of SWOT from its high-resolution 2-D SSH observations in advancing seamount detection via satellite-derived VGG. We identify challenges and recommend improved detections using data integration and machine learning. [ABSTRACT FROM AUTHOR]

In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m. [ABSTRACT FROM AUTHOR]

Matching optical and synthetic aperture radar (SAR) images is often challenged by intricate geometric distortion and nonlinear radiation differences, leading to insufficient and unevenly distributed corresponding points. To tackle this issue, we propose a lightweight deep convolutional network with inverted residuals for optical and SAR image matching. Initially, a fully convolutional neural network (FCNN) is designed to extract high-level and semantic features, robustly capturing universal characteristics between optical and SAR images, adept at handling geometric distortion and nonlinear radiation changes. Notably, we integrate a lightweight architecture with inverted residuals into FCNN to adeptly extract local and global contextual information, facilitating feature reuse and minimizing the loss of crucial features. Additionally, a vector-refined module is deployed to refine dense features, filtering out redundant information. Subsequently, a coarse-to-fine strategy is employed to further eliminate gross errors or incorrect matches. Finally, we evaluate the performance of the proposed network in optical and SAR image matching against manually-designed methods and state-of-the-art deep learning techniques. Experimental results demonstrate that our network significantly surpasses existing methods in terms of the number of correct matches and matching accuracy. Specifically, our proposed network achieves at least a 2.8 times increase in correct matches and an 18% improvement in matching accuracy compared to existing methods. [ABSTRACT FROM AUTHOR]

As the variety of space targets expands, two-dimensional (2D) ISAR images prove insufficient for target recognition, necessitating the extraction of three-dimensional (3D) information. The 3D geometry reconstruction method utilizing energy accumulation of ISAR image sequence (ISEA) facilitates superior reconstruction while circumventing the laborious steps associated with factorization methods. Nevertheless, ISEA's neglect of valid information necessitates a high quantity of images and elongated operation times. This paper introduces a partitioned parallel 3D reconstruction method utilizing sorted-energy semi-accumulation with ISAR image sequences (PP-ISEA) to address these limitations. The PP-ISEA innovatively incorporates a two-step search pattern—coarse and fine—that enhances search efficiency and conserves computational resources. It introduces a novel objective function 'sorted-energy semi-accumulation' to discern genuine scatterers from spurious ones and establishes a redundant point exclusion module. Experiments on the scatterer model and simulated electromagnetic model demonstrate that the PP-ISEA reduces the minimum image requirement from ten to four for high-quality scatterer model reconstruction, thereby offering superior reconstruction quality in less time. [ABSTRACT FROM AUTHOR]

In order to achieve the remote application of inverse synthetic aperture laser radar for high resolution spatial situational awareness, it is essential to analyze the main factors that restrict its remote application. This study combines the range equation of inverse synthetic aperture lidar with the stimulated Brillouin threshold power equation to investigate the variation of laser transmitting power with distance. Additionally, by utilizing the excited Brillouin threshold power equation, laser linewidth formula, and pulse width characteristics of pulse signal, we examine the variation law of laser coherence that meets corresponding power requirements at different distances. The results indicate that a detection distance of 22 km and below can be achieved using continuous fiber lasers without compensation. Coherence compensation is necessary for distances between 22 km and 57 km. For distances ranging from 57 km to 3000 km, pulsed solid-state lasers are used to analyze coherence and conclude that imaging non-cooperative targets within this range is feasible. It is observed that coherence compensation is required from 57 km to 2179 km, becoming more challenging after 2000 km. Furthermore, pulsed solid-state lasers can still be utilized for imaging cooperative targets within a range of 2179–3273 km; however, coherence compensation remains necessary and becomes increasingly difficult. Finally, several coherent length compensation schemes are proposed in order to extend the imaging range of inverse synthetic aperture LiDAR to approximately 3000 km. [ABSTRACT FROM AUTHOR]

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging with azimuth multi-channel always suffers from channel phase and amplitude errors. Compared with spatial-invariant error, the range-dependent channel phase error is intractable due to its spatial dependency characteristic. This paper proposes a novel parameterized channel equalization approach to reconstruct the unambiguous SAR imagery. First, a linear model is established for the range-dependent channel phase error, and the sharpness of the reconstructed Doppler spectrum is used to measure the unambiguity quality. Furthermore, the intrinsic relationship between the channel phase errors and the sharpness is revealed, which allows us to estimate the optimal parameters by maximizing the sharpness of the reconstructed Doppler spectrum. Finally, the results from real-measured data show that the suggested method performs exceptionally for ambiguity suppression in HRWS SAR imaging. [ABSTRACT FROM AUTHOR]

Rainbands are essential to tropical cyclones (TCs), significantly affecting TC structure and intensity change. High‐resolution synthetic aperture radar (SAR) imagery can capture the footprints of rainbands caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we investigated the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwest Pacific than in the North Atlantic and Northeast Pacific. Plain Language Summary: Rainbands are a salient feature of tropical cyclones (TCs) and are closely related to TC structure and intensity change. Synthetic aperture radar (SAR) can capture the sea surface imprint of rainbands beneath clouds caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we made 464 rainband‐annotated data. The data were mapped to grid nodes spaced at 0.027 times the radius of max winds in a coordinate system with the origin at the TC center and the y‐axis in the vertical wind shear direction. Then, the data were composited to estimate and further investigate the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwestern Pacific than in the North Atlantic and Northeast Pacific. Key Points: The sea surface imprint of tropical cyclone (TC) rainbands in many synthetic aperture radar images reveals their occurrence probabilityThe rainband‐occurrence probability is overall higher in the early morning than in the evening. The feature is more obvious in strong TCsThe peak region of the probability appears farther from the TC center in the evening than in the early morning [ABSTRACT FROM AUTHOR]

Where's Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of Ziphius cavirostris. We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins. Author summary: Reconstructing the movement of animals from their vocalizations is a powerful method to observe their behavior in situations where visual monitoring is impractical. Arrays of acoustic recording devices can be used to determine the location of vocalizing animals and a series of locations can be linked to form tracks. However, reconstructing tracks requires methods of determining which animal in a group is vocalizing, finding the same vocalization on multiple recording devices, and determining the most likely location of the animal based on the relative times the sound arrived at various recording devices. We have developed a toolkit called Where's Whaledo to assist researchers in reconstructing the behavior of these animals using arrays of acoustic recording devices. This toolkit greatly accelerates the process of reconstructing their tracks using a combination of automated processes and user interfaces. We use Where's Whaledo to reconstruct the tracks of deep-diving beaked whales (Ziphius cavirostris). We successfully reconstruct tracks of groups of up to five whales vocalizing concurrently. [ABSTRACT FROM AUTHOR]

With the number of new satellites increasing dramatically, comprehensive space surveillance is becoming increasingly important. Therefore, high-resolution inverse synthetic aperture radar (ISAR) imaging of satellites can provide an in-situ assessment of the satellites. This paper demonstrates that pseudo-noise signals can also be used for satellite imaging, in addition to classical linear frequency-modulated chirp signals. Pseudo-noise transmission signals offer the advantage of very low cross-correlation values. This, for instance, enables the possibility of a system with multiple channels transmitting instantaneously. Furthermore, it can significantly reduce signal interference with other systems operating in the same frequency spectrum, which is of particular interest for high-bandwidth, high-power systems such as satellite imaging radars. A new routine has been introduced to generate a wideband pseudo-noise signal with a peak-to-average power ratio (PAPR) similar to that of a chirp signal. This is essential for applications where the transmit signal power budget is sharply limited by the high-power amplifier. The paper presents both theoretical descriptions and analysis of the generated pseudo-noise signal as well as the results of an imaging measurement of a real space target using the introduced pseudo-noise signals. [ABSTRACT FROM AUTHOR]

Characterizing the coseismic slip behaviors of earthquakes could offer a better understanding of regional crustal deformation and future seismic potential assessments. On 18 December 2023, an Mw 6.0 earthquake occurred on the Lajishan–Jishishan fault system (LJFS) in the northeastern Tibetan Plateau, causing serious damage and casualties. The seismogenic fault hosting this earthquake is not well constrained, as no surface rupture was identified in the field. To address this issue, in this study, we use Interferometric Synthetic Aperture Radar (InSAR) data to investigate the coseismic surface deformation of this earthquake and invert both ascending and descending line-of-sight observations to probe the seismogenic fault and its slip characteristics. The InSAR observations show up to ~6 cm surface uplift caused by the Jishishan earthquake, which is consistent with the thrust-dominated focal mechanism. A Bayesian-based dislocation modeling indicates that two fault models, with eastern and western dip orientations, could reasonably fit the InSAR observations. By calculating the coseismic Coulomb failure stress changes (∆CFS) induced by both fault models, we find that the east-dipping fault scenario could reasonably explain the aftershock distributions under the framework of stress triggering, while the west-dipping fault scenario produced a negative ∆CFS in the region of dense aftershocks. Integrating regional geological structures, we suggest that the seismogenic fault of the Jishishan earthquake, which strikes NNE with a dip of 56° to the east, may be either the Jishishan western margin fault or a secondary buried branch. The optimal finite-fault slip modeling shows that the coseismic slip was dominated by reverse slip and confined to a depth range between ~5 and 15 km. The released seismic moment is 1.61 × 1018 N·m, which is equivalent to an Mw 6.07 earthquake. While the Jishishan earthquake ruptured a fault segment of approximately 20 km, it only released a small part of the seismic moment that was accumulated along the 220 km long Lajishan–Jishishan fault system. The remaining segments of the Lajishan–Jishishan fault system still have the capability to generate moderate-to-large earthquakes in the future. [ABSTRACT FROM AUTHOR]

Groundwater resources are crucial to socio-economic development and the ecosystem, and over-extraction can cause the groundwater level to drop, deplete reserves, and trigger geological hazards like land subsidence. The North China Plain (NCP) has experienced both subsidence and groundwater depletion due to over-extraction in the past 70 years. In this study, we used MT-InSAR technology and ascending C-band Sentinel-1 SAR data from 2017 to 2023 to study land deformation in the junction area of Shijiazhuang–Baoding–Cangzhou–Hengshui. We identified multiple subsidence funnels with a maximum rate exceeding −150 mm/year and a total deformation surpassing 600 mm. Seasonal decomposition methods accurately separated seasonal signals in the time-series deformation and groundwater level data. An exponential function model applied to long-term deformation showed no significant decrease in subsidence in severely affected areas. By modeling seasonal deformation and seasonal groundwater levels, we determined the elastic skeletal storage coefficients (Ske) to be in the range of 1.02 × 10−3~6.53 × 10−3 in subsidence areas. We obtained the spatiotemporal evolution of the total groundwater storage (TGWS), irreversible ground storage (IGWS), and recoverable ground storage (RGWS). The TGWS and IGWS decreased annually while the RGWS increased, which is attributable to the implementation of the South-to-North Water Diversion Project (SNWDP) and the issuance of groundwater withdrawal policies in the NCP. [ABSTRACT FROM AUTHOR]

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management. [ABSTRACT FROM AUTHOR]

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results. [ABSTRACT FROM AUTHOR]

Tomographic Synthetic Aperture Radar (TomoSAR) building object height inversion is a sparse reconstruction problem that utilizes the data obtained from several spacecraft passes to invert the scatterer position in the height direction. In practical applications, the number of passes is often small, and the observation data are also small due to the objective conditions, so this study focuses on the inversion under the restricted observation data conditions. The Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA) is a kind of deep unfolding network algorithm, which is a combination of the Iterative Shrinkage Thresholding Algorithm (ISTA) and deep learning technology, and it has the advantages of both. The ALISTA is one of the representative algorithms for TomoSAR building object height inversion. However, the structure of the ALISTA algorithm is simple, which has neither the excellent connection structure of a deep learning network nor the acceleration format combined with the ISTA algorithm. Therefore, this study proposes two directions of improvement for the ALISTA algorithm: firstly, an improvement in the inter-layer connection of the network by introducing a connection similar to residual networks obtains the Extragradient Analytic Learned Iterative Shrinkage Thresholding Algorithm (EALISTA) and further proves that the EALISTA achieves linear convergence; secondly, there is an improvement in the iterative format of the intra-layer iteration of the network by introducing the Nesterov momentum acceleration, which obtains the Fast Analytic Learned Iterative Shrinkage Thresholding Algorithm (FALISTA). We first performed inversion experiments on simulated data, which verified the effectiveness of the two proposed algorithms. Then, we conducted TomoSAR building object height inversion experiments on limited measured data and used the deviation metric P to measure the robustness of the algorithms to invert under restricted observation data. The results show that both proposed algorithms have better robustness, which verifies the superior performance of the two algorithms. In addition, we further analyze how to choose the most suitable algorithms for inversion in engineering practice applications based on the results of the experiments on measured data. [ABSTRACT FROM AUTHOR]

Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%. [ABSTRACT FROM AUTHOR]

Multistatic synthetic aperture radar (SAR) is a special mode of SAR system. The radar transmitter and receiver are located on different satellites, which brings many advantages, such as flexible baseline configuration, diverse receiving modes, and more detailed ground object classification information. The multistatic SAR has been widely used in interferometry, moving target detection, three-dimensional imaging, and other fields. The frequency offset between different oscillators will cause a modulation phase error in the signal. Therefore, phase synchronization is one of the most critical problems to be addressed in distributed SAR systems. This article reviews phase synchronization techniques, which are mainly divided into two methods: synchronization by direct microwave link and synchronization by a data-based estimation algorithm. Furthermore, the future development of synchronization technology is anticipated. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) is an advanced microwave imaging technology that provides all-weather and all-day target information. However, as spaceborne SAR resolution improves, traditional echo signal models based on airborne SAR design become inadequate due to the curved orbit, Earth rotation, and increased propagation distance. In this study, we propose an accurate range model for high-resolution spaceborne SAR by analyzing motion trajectory and Doppler parameters from the perspective of the space geometry of spaceborne SAR. We evaluate the accuracy of existing range models and propose an advanced equivalent squint range model (AESRM) that accurately fits the actual range history and compensates for high-order term errors by introducing third-order and fourth-order error terms while maintaining the simplicity of the traditional model. The proposed AESRM's concise two-dimensional frequency spectrum form facilitates the design of imaging algorithms. Point target simulations confirm the effectiveness of the proposed AESRM, demonstrating significant improvements in fitting accuracy for range histories characterized by nonlinear trajectories. The developed AESRM provides a robust foundation for designing imaging algorithms and enables higher resolution and more accurate radar imaging. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

The quest for higher spatial and/or temporal resolution in functional MRI (fMRI) while preserving a sufficient temporal signal-to-noise ratio (tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs. 3D acquisition strategies, parallel imaging and/or compressed sensing (CS) accelerations and simultaneous multi-slice acquisitions to cite a few. In this paper, we investigate the use of a finely tuned version of 3D-SPARKLING. This is a non-Cartesian CS-based acquisition technique for high spatial resolution whole-brain fMRI. We compare it to state-of-the-art Cartesian 3D-EPI during both a retinotopic mapping paradigm and resting-state acquisitions at 1mm3 (isotropic spatial resolution). This study involves six healthy volunteers and both acquisition sequences were run on each individual in a randomly-balanced order across subjects. The performances of both acquisition techniques are compared to each other in regards to tSNR, sensitivity to the BOLD effect and spatial specificity. Our findings reveal that 3D-SPARKLING has a higher tSNR than 3D-EPI, an improved sensitivity to detect the BOLD contrast in the gray matter, and an improved spatial specificity. Compared to 3D-EPI, 3D-SPARKLING yields, on average, 7% more activated voxels in the gray matter relative to the total number of activated voxels. [ABSTRACT FROM AUTHOR]

Efficient discrimination of diverse kharif crops, remains crucial for crop monitoring and production forecasting, and plays a pivotal role in decision-making for food security in India. This study aims to harness temporal backscatter data from EOS-04 C-band synthetic aperture data (SAR) payload to achieve precise discrimination among six short-duration (cereal, oilseeds, fibre) and long-duration (fibre, pulses) kharif crops. The study integrates limited ground-truth polygons and a Random Forest machine learning approach for analysing EOS-04 time-series data. The classification accuracies were found to be higher than 75% across all kharif crops, with cereals exhibiting the highest accuracy, succeeded by fibre, oilseed and pulse crops. A key focus lies in identifying optimal polarization combinations for effective discrimination among diverse kharif crop types. The study reveals that the synergistic utilization of dual polarizations outperforms individual co- or cross-polarizations, notably benefiting discrimination of cotton, soybean and groundnut crops. Horizontal–vertical polarizations are found to be most effective for achieving peak accuracies in rice and red gram crops. Furthermore, the analysis indicates a promising potential for early crop assessment, presenting an opportunity to furnish precise crop estimates at least one and a half months before the harvest. [ABSTRACT FROM AUTHOR]

Indian Space Research Organisation (ISRO) has successfully launched its second civilian C-band Synthetic Aperture Radar (SAR) mission onboard Earth Observation Satellite-04 (EOS-04). The SAR data monitors and measures various atmospheric features and parameters. In this paper, we report on the investigation of EOS-04 data for several atmospheric phenomena. One of the crucial parameters for studying atmospheric manifestations in SAR data is ocean surface winds and an algorithm for its retrieval has been developed using EOS-04 data. The wind speed products thus generated are evaluated using observations from the Advanced Scatterometer and subsequently used to study atmospheric phenomena like boundary layer structures. The EOS-04 SAR data is also demonstrated for studying structures associated with tropical cyclones, coupling of rain and wind imprints and distinct signatures of an atmospheric front. The study outcomes are used to interpret atmospheric phenomena and understand backscattering signals from EOS-04 SAR. This indicates the possibility and potential of a gamut of atmospheric phenomena that can be probed using EOS-04 SAR data. [ABSTRACT FROM AUTHOR]

Synthetic aperture radars (SARs) have enabled allweather sensing of land surfaces, which has significantly benefitted surface hydrology. In this article, we explore various hydrological applications enabled by Indian Space Research Organisation’s (ISRO’s) recently launched Earth observation satellite-04 (EOS-04) C-band SAR satellite. We present the preliminary results of EOS-04 driven flood inundation mapping, monitoring of water bodies and study of river dynamics. Novel application of EOS-04 SAR data in estimation of sedimentation rate is presented for Jayakwadi reservoir, Maharashtra, India. Additionally, this article explores the integrated use of SAR and altimeter data for estimation of lake and reservoir water level and volume. We have used multidate observations from EOS-04 SAR and Sentinel-3A/ 3B altimeters to derive river width and estimate area– water level hypsometric curves for reservoirs. Long-term and near real-time availability of EOS-04 data can provide an indispensable tool for monitoring water bodies and extreme events like floods, at regional scales. [ABSTRACT FROM AUTHOR]

In dry southwestern South Tyrol, Italy, rock glaciers are dominant landforms of the high-mountain cryosphere. Their spatial distribution and degree of activity hold critical information on the current state of discontinuous permafrost and consequently on the response potential to climate warming. Traditional geomorphologic mapping, however, owing to the qualitative expert-based nature, typically displays a high degree of uncertainty and variability among operators with respect to the dynamic classification of intact (permafrost-bearing) and relict (permafrost-devoid) rock glaciers. This limits the reliability of geomorphologic rock glacier inventories for basic and applied purposes. To address this limitation, (i) we conduct a systematic evaluation of the improvements that synthetic aperture radar interferometry (InSAR) can afford to the detection and dynamic classification of rock glaciers and (ii) build an integrated inventory that combines the strengths of geomorphologic- and InSAR-based approaches. To exploit fully InSAR-based information towards a better understanding of the topo-climatic conditions that sustain creeping permafrost, we further explore how velocity and the spatial distribution of moving areas (MAs) within rock glaciers may vary as a function of simple topographic variables known to exert first-order controls on incoming solar radiation, such as elevation and aspect. Starting from a geomorphologic inventory (n=789), we characterize the kinematics of InSAR-based MAs and the relevant hosting rock glaciers on 36 Sentinel-1 interferograms in the 2018–2019 period. With respect to the original inventory, InSAR analysis allowed us to identify 14 previously undetected rock glaciers. Further, it confirmed that 246 (76 %) landforms, originally interpreted as intact, do exhibit detectable movement (i.e., ≥1 cm yr -1) and that 270 (60 %) of the relict labeled counterparts do not, whereas 144 (18 %) were kinematically undefined due to decorrelation. Most importantly, InSAR proved critical for reclassifying 121 (15 %) rock glaciers, clarifying that 41 (13 %) of those interpreted as being intact do not exhibit detectable movement and that 80 (17 %) of the original relict ones do move. Reclassification (i) allowed us to identify a cluster of intact rock glaciers below 2000 m a.s.l. associated with positive mean annual air temperature (MAAT), and (ii) by increasing the altitudinal overlap between intact and relict rock glaciers, it depicts a broad transition belt in the aspect–elevation space, which varies from 50 m on west-facing slopes to 500 m on easterly ones. This finding deteriorates the significance of elevation and aspect as topographic proxies for modeling permafrost occurrence and highlights the importance of using InSAR to inform such models. From a process-oriented standpoint, InSAR information proves fundamental for imaging how this altitudinal transition manifests through changing rates and styles of rock glacier surface deformation. Specifically, we find that, as rock glaciers move faster, an increasingly larger proportion of their surface becomes kinematically involved (i.e., percent MA cover) and that this proportion increases with elevation up to 2600–2800 m, beyond which an inflection occurs and consistent average values are attained. Considering that the inflection falls between the -1 and -2 °C MAAT – the lower boundary for discontinuous permafrost – and is independent of slope gradient, we conclude that this altitudinal pattern represents a geomorphic signature: the dynamic expression of increasing permafrost distribution, from sporadic to discontinuous. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar tomography (TomoSAR) is an extension of synthetic aperture radar (SAR) imaging. It introduces the synthetic aperture principle into the elevation direction to achieve three-dimensional (3-D) reconstruction of the observed target. Compressive sensing (CS) is a favorable technology for sparse elevation recovery. However, for the non-sparse elevation distribution of the forested areas, if CS is selected to reconstruct it, it is necessary to utilize some orthogonal bases to first represent the elevation reflectivity sparsely. The iterative adaptive approach (IAA) is a non-parametric algorithm that enables super-resolution reconstruction with minimal snapshots, eliminates the need for hyperparameter optimization, and requires fewer iterations. This paper introduces IAA to tomographicinversion of the forested areas and proposes a novel multi-polarimetric-channel joint 3-D imaging method. The proposed method relies on the characteristics of the consistent support of the elevation distribution of different polarimetric channels and uses the L 2 -norm to constrain the IAA-based 3-D reconstruction of each polarimetric channel. Compared with typical spectral estimation (SE)-based algorithms, the proposed method suppresses the elevation sidelobes and ambiguity and, hence, improves the quality of the recovered 3-D image. Compared with the wavelet-based CS algorithm, it reduces computational cost and avoids the influence of orthogonal basis selection. In addition, in comparison to the IAA, it demonstrates greater accuracy in identifying the support of the elevation distribution in forested areas. Experimental results based on BioSAR 2008 data are used to validate the proposed method. [ABSTRACT FROM AUTHOR]

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results. [ABSTRACT FROM AUTHOR]

The imaging of high-speed high-squint synthetic aperture radar (HSHS-SAR), which is mounted on maneuvering platforms with curved trajectory, is a challenging task due to the existence of 3-D acceleration and the azimuth spatial variability of range migration and Doppler parameters. Although existing imaging algorithms based on linear range walk correction (LRWC) and nonlinear chirp scaling (NCS) can reduce the range–azimuth coupling of the frequency spectrum (FS) and the spatial variability of the Doppler parameter to some extent, they become invalid as the squint angle, speed, and resolution increase. Additionally, most of them ignore the effect of acceleration phase calibration (APC) on NCS, which should not be neglected as resolution increases. For these issues, a modified frequency nonlinear chirp scaling (MFNCS) algorithm is proposed in this paper. The proposed MFNCS algorithm mainly includes the following aspects. First, a more accurate approximation of range model (MAARM) is established to improve the accuracy of the instantaneous slant range history. Second, a preprocessing of the proposed algorithm based on the first range compression, LRWC, and a spatial-invariant APC (SIVAPC) is implemented to eliminate most of the effects of high-squint angle and 3-D acceleration on the FS. Third, a spatial-variant APC (SVAPC) is performed to remove azimuth spatial variability introduced by 3-D acceleration, and the range focusing is accomplished by the bulk range cell migration correction (BRCMC) and extended secondary range compression (ESRC). Fourth, the azimuth-dependent characteristics evaluation based on LRWC, SIVAPC, and SVAPC is completed to derive the MFNCS algorithm with fifth-order chirp scaling function for azimuth compression. Consequently, the final image is focused on the range time and azimuth frequency domain. The experimental simulation results verify the effectiveness of the proposed algorithm. With a curved trajectory, HSHS-SAR imaging is carried out at a 50° geometric squint angle and 500 m × 500 m imaging width. The integrated sidelobe ratio and peak sidelobe ratio of the point targets at the scenario edges approach the theoretical values, and the range-azimuth resolution is 1.5 m × 3.0 m. [ABSTRACT FROM AUTHOR]

Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations' data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city's gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China. [ABSTRACT FROM AUTHOR]

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform's position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS). [ABSTRACT FROM AUTHOR]

The utilization of deep learning in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has witnessed a recent surge owing to its remarkable feature extraction capabilities. Nonetheless, deep learning methodologies are often encumbered by inadequacies in labeled data and the protracted nature of training processes. To address these challenges and offer an alternative avenue for accurately extracting image features, this paper puts forth a novel and distinctive network dubbed the Capsule Broad Learning System Network for robust SAR ATR (CBLS-SARNET). This novel strategy is specifically tailored to cater to small-sample SAR ATR scenarios. On the one hand, we introduce a United Division Co-training (UDC) Framework as a feature filter, adeptly amalgamating CapsNet and the Broad Learning System (BLS) to enhance network efficiency and efficacy. On the other hand, we devise a Parameters Sharing (PS) network to facilitate secondary learning by sharing the weight and bias of BLS node layers, thereby augmenting the recognition capability of CBLS-SARNET. Experimental results unequivocally demonstrate that our proposed CBLS-SARNET outperforms other deep learning methods in terms of recognition accuracy and training time. Furthermore, experiments validate the generalization and robustness of our novel method under various conditions, including the addition of blur, Gaussian noise, noisy labels, and different depression angles. These findings underscore the superior generalization capabilities of CBLS-SARNET across diverse SAR ATR scenarios. [ABSTRACT FROM AUTHOR]

The emerging technology of rotating synthetic aperture (RSA) presents a promising solution for the development of lightweight, large-aperture, and high-resolution optical remote sensing systems in geostationary orbit. However, the rectangular shape of the primary mirror and the distinctive imaging mechanism involving the continuous rotation of the mirror lead to a pronounced decline in image resolution along the shorter side of the rectangle compared to the longer side. The resolution also exhibits periodic time-varying characteristics. To address these limitations and enhance image quality, we begin by analyzing the imaging mechanism of the RSA system. Subsequently, we propose a single-image super-resolution method that utilizes a rotated varied-size window attention mechanism instead of full attention, based on the Vision Transformer architecture. We employ a two-stage training methodology for the network, where we pre-train it on images masked with stripe-shaped masks along the shorter side of the rectangular pupil. Following that, we fine-tune the network using unmasked images. Through the strip-wise mask sampling strategy, this two-stage training approach effectively circumvents the interference of lower confidence (clarity) information and outperforms training the network from scratch using the unmasked degraded images. Our digital simulation and semi-physical imaging experiments demonstrate that the proposed method achieves satisfactory performance. This work establishes a valuable reference for future space applications of the RSA system. [ABSTRACT FROM AUTHOR]

In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an innovative artificial material cloak in SAR target jamming presents augmented capabilities. These methods demonstrate a proficient redirection of incident EM waves in specific or arbitrary directions, effectively masking the vital information linked to critical targets. This study introduces a broadband SAR target jamming system employing an information metasurface that incorporates intelligent information processing algorithms in conjunction with a space-time-coding digital metasurface, endowing it with the capacity to adeptly modulate incident EM waves. This integration facilitates a versatile approach to jamming, enabling the deployment of multi-mode protective measures against critical targets. The conducted simulation and experiment results validate the system's ability to adjustably produce EM deception and generate multiple false targets independently of the SAR system. The outcomes of this research significantly advance the practicality of SAR protection strategies, pushing the boundaries toward more dynamic, broadband, and controllable scenarios, thereby substantially improving the concealment of critical targets in highly sensitive conflict areas. [ABSTRACT FROM AUTHOR]

Due to a legacy originating in the limited capability of early computers, the spectroscopic resolution used in Fourier transform infrared spectroscopy and other systems has largely been implemented using only powers of two for more than 50 years. In this study, we investigate debunking the spectroscopic lore of, e.g., using only 2, 4, 8, or 16 cm−1 resolution and determine the optimal resolution in terms of both (i) a desired signal-to-noise ratio and (ii) efficient use of acquisition time. The study is facilitated by the availability of solids and liquids reference spectral data recorded at 2.0 cm−1 resolution and is based on an examination in the 4000–400 cm−1 range of 61 liquids and 70 solids spectra, with a total analysis of 4237 peaks, each of which was also examined for being singlet/multiplet in nature. Of the 1765 liquid bands examined, only 27 had widths <5 cm−1. Of the 2472 solid bands examined, only 39 peaks have widths <5 cm−1. For both the liquid and solid bands, a skewed distribution of peak widths was observed: For liquids, the mean peak width was 24.7 cm−1 but the median peak width was 13.7 cm−1, and, similarly, for solids, the mean peak width was 22.2 cm−1 but the median peak width was 11.2 cm−1. While recognizing other studies may differ in scope and limiting the analysis to only room temperature data, we have found that a resolution to resolve 95% of all bands is 5.7 cm−1 for liquids and 5.3 cm−1 for solids; such a resolution would capture the native linewidth (not accounting for instrumental broadening) for 95% of all the solids and liquid bands, respectively. After decades of measuring liquids and solids at 4, 8, or 16 cm−1 resolution, we suggest that, when accounting only for intrinsic linewidths, an optimized resolution of 6.0 cm−1 will capture 91% of all condensed-phase bands, i.e., broadening of only 9% of the narrowest of bands, but yielding a large gain in signal-to-noise with minimal loss of specificity. [ABSTRACT FROM AUTHOR]

Background: Previously, there has been some work in the field of optical imaging on phase compensation by employing Legendre polynomials as an expansion of the phase function, and this seems to be an appealing unstudied area in the field of ultrasound imaging. Purpose: The paper is devoted to solving one of the problems of enhancing the authenticity of diagnostics data obtained in ultrasound visualization systems and presents a novel approach to aberration correction, which ensures a reliable eliminating of phase distortions. The novelty of the proposed approach consists in the use of the decomposition of the wave front by Legendre polynomials to approximate aberrations of the phase front of ultrasonic waves propagating through distorting layers. Methods: The phase aberrations are corrected using a single sector probe that captures echoes in synthetic aperture mode. The proposed method approximates the changes in the wave front by means of the Legendre polynomials. The performance was investigated by placing a 13‐mm‐wide ultrasonic probe with 64 elements operating at 2 MHz on the surface of a commercial quality control phantom ATS Model 539 through a distorting layer. The following metrics were measured: peak value, root mean square (RMS), full width at half maximum (FWHM), contrast‐to‐noise ratio (CNR). During the experiments, three different aberrators were used interchangeably as distorting layers, two of which were made of photopolymer resin with RMS values of 39 and 97 ns and a speed of sound close to that of a human temporal bone tissue, and the third was an ex vivo human temporal bone with the RMS value of 44 ns. To test the correction, three regions inside the phantom were examined. Two‐sided nonparametric Wilcoxon rank‐sum test was used for assessing the statistical significance. The significance level for this study was set at α ≤ 0.05. To counteract the problem of multiple comparisons, the p‐values were adjusted by the Holm–Bonferroni correction method. Results: The statistically significant results have demonstrated the possibility of increasing peak intensity value up to 3.08 times, reducing the RMS width and FWHM of the intensity angular distribution down to 60% and 82%, respectively, and increasing CNR up to 2.12 times by using the proposed phase distortion correction method, compared with the case without correction. The results show that the method can be used as an effective aberration correction technique for all tested regions inside the phantom and distorting layers. Its usage allowed correcting up to 97% of the aberrations caused by the ex vivo temporal bone model. Conclusion: The results show that the usage of the proposed method for aberration correction can successfully increase the intensity and reduce the angular width of the ultrasound wave scattered by the point targets inside the phantoms. The method works with different distorting layers and is capable of correcting phase aberrations in multiple sections of the sonogram. The limitation of the proposed method consists in the fact that it requires the use of aperture synthesis and access to raw radiofrequency data, which restricts its application in common scanners. [ABSTRACT FROM AUTHOR]

In PROPELLER MRI, obtaining sufficient high-quality blade data remains a challenge, so the efficiency and generalization of deep learning-based reconstruction models are deteriorated. Due to narrow rotated and translated blades acquired in PROPELLER, the technique of data augmentation that is used for deep learning-based Cartesian MRI reconstruction cannot be directly applied. To address the issue, this paper introduces a novel approach for the generation of synthetic PROPELLER blades, and it is subsequently employed in data augmentation for undersampled blades reconstruction. The principal aim of this study is to address the challenges of reconstructing undersampled blades to enhance both image quality and computational efficiency. Evaluation metrics including PSNR, NMSE, and SSIM indicate superior performance of the model trained with augmented data compared to non-augmented counterparts. The synthetic blade augmentation significantly enhances the model's generalization capability and enables robust performance across varying imaging conditions. Furthermore, the study demonstrates the feasibility of utilizing synthetic blades exclusively in the training phase, suggesting a reduced dependency on real PROPELLER blades. This innovation in synthetic blade generation and data augmentation technique contributes to enhanced image quality and improved generalization capability of the associated deep learning model for PROPELLER MRI reconstruction. [ABSTRACT FROM AUTHOR]

In image processing, image segmentation is the process of partitioning a digital image into multiple image segments. Among state‐of‐the‐art methods, Markov random fields can be used to model dependencies between pixels and achieve a segmentation by minimising an associated cost function. Currently, finding the optimal set of segments for a given image modelled as a Markov random fields appears to be NP‐hard. The authors aim to take advantage of the exponential scalability of quantum computing to speed up the segmentation of synthetic aperture radar images. For that purpose, the authors propose a hybrid quantum annealing classical optimisation expectation maximisation algorithm to obtain optimal sets of segments. After proposing suitable formulations, the authors discuss the performances and the scalability of authors' approach on the D‐Wave quantum computer. The authors also propose a short study of optimal computation parameters to enlighten the limits and potential of the adiabatic quantum computation to solve large instances of combinatorial optimisation problems. [ABSTRACT FROM AUTHOR]

Accurate and robust time delay estimation is crucial for synthetic aperture sonar (SAS) imaging. A two‐step time delay estimation method based on displaced phase centre antenna (DPCA) micronavigation has been widely applied in motion estimation and compensation of SASs. However, the existing methods for time delay estimation are not sufficiently robust, which reduces the performance of SAS motion estimation. Deep learning is currently one of the cutting‐edge techniques and has brought about a remarkable progress in the field of underwater acoustic signal processing. In this study, a deep learning‐based time delay estimation method is introduced to SAS motion estimation and compensation. The subband processing is first applied to obtain ambiguous time delays between adjacent pings from phases of SAS echoes. Then, a lightweight neural network is utilised to construct phase unwrapping. The model of the employed neural network is trained with simulation data and applied to real SAS data. The results of time delay estimation and motion compensation demonstrate that the proposed neural network‐based method has much better performance than the two‐step and joint‐subband methods. [ABSTRACT FROM AUTHOR]

Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA's Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests. [ABSTRACT FROM AUTHOR]

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced. [ABSTRACT FROM AUTHOR]

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings. [ABSTRACT FROM AUTHOR]

Frequency synchronization error, as one of the inevitable technical challenges in distributed synthetic aperture radar (SAR), has different impacts on different SAR systems. Multi-monostatic SAR is a typical distributed configuration where frequency synchronization errors are tiny in distributed airborne and low earth orbit (LEO) SAR systems. However, due to the long time delay and long synthetic aperture time, the imaging performance of a multi-monostatic geosynchronous (GEO) SAR system is affected by frequency oscillator errors. In this paper, to investigate the frequency synchronization problem in this configuration, we firstly model the echo signals with the frequency synchronization errors, which can be divided into fixed frequency errors and random phase noise. Secondly, we talk about the impacts of the two kinds of errors on imaging performance. To solve the problem, we thirdly propose an autofocus back-projection (ABP) algorithm, which adopts the coordinate descent method and iteratively adjusts the phase error estimation until the image reaches its maximum sharpness. Based on the characteristics of the frequency synchronization errors, we further propose the Node ABP (NABP) algorithm, which greatly reduces the amount of storage and computation compared to the ABP algorithm. Finally, simulations are carried out to validate the effectiveness of the ABP and NABP algorithms. [ABSTRACT FROM AUTHOR]