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Radar technology is critical for subsurface sensing applications since it enables the extraction of surface information remotely without being affected by environmental factors such as illumination and weather conditions. Hence, their usage on drones has become extremely essential as these vehicles need to accumulate data while flying over different and possibly difficult-to-access terrains regardless of visibility conditions. A frequency-modulated continuous-wave (FMCW) single input multiple output (SIMO) radar system is developed to provide near real-time radar images onboard. The system is comprised of a transceiver module, specifically designed for synthetic aperture radar (SAR) applications, to produce images in real-time. GPU-based computation is utilized to optimize processing power, while enabling range profiles to be generated and SIMO SAR images to be created during flight. The system can be easily mounted on UAV or multirotor systems. The detection and ranging performance of the system are examined by performing several experiments at the campus. Objects up to 100 metres are successfully detected by the radar system. The system demonstrates excellent ability to generate 2D SIMO SAR images, effectively identifying building and guard-box exteriors that are consistent with images from Google Earth. [ABSTRACT FROM AUTHOR]

P-band radar interferometry is a promising tool for digital terrain modelling in forested areas such as the Amazon due to the ability of P-band to penetrate densely forested areas. The quality of Digital Terrain Models (DTMs) is a primary requirement for many geoscience applications because DTM accuracy is an essential variable for characterizing landform shapes. This article presents a multicriteria evaluation of the overall quality of a digital terrain model produced using P-band SAR interferometric processing. The evaluation considers behaviours related to elevation and slope and analyses the impact of landscape properties such as slope and aspect. The multicriteria accuracy of the study showed promising results about the realism of landforms, according to applied geomorphological criteria, and also in the visual comparison between the produced DTM and the reference. The study provided an estimate of the accuracy of P-band radar DTM in terms of elevation with a standard deviation of 2.36 m and in terms of slope with a mean error of approximately −4°, a standard deviation of 4.74°, and a RMSE (Root Mean Square Error) slope error of 6.14°, using a reference DTM derived from an airborne LiDAR (Light Detection and Ranging) survey. The spatial behaviour of these errors and their sensitivity to slope and aspect were analysed. The investigation of hydrographic inconsistencies in the DTM was conducted based on various criteria, including identifying depressions along watercourses and adhering to Horton's law. Finally, the effects of the acquisition, processing, and resampling processes are revealed through indicators such as the rose histogram. This multicriteria study demonstrates the suitability of airborne P-band radar interferometry for digital elevation modelling in tropical rainforest environments. [ABSTRACT FROM AUTHOR]

Polar lows are small, high-latitude, intense maritime cyclones and frequently have severe impacts on the ocean such as strong winds, high waves and heavy rainfall. They are difficult to observe and forecast due to their short lifetime (<48 hours), small horizontal scales (200 ~ 1000 km), and the sparse synoptic observing network that exists in the subarctic and Arctic oceans. Previous studies have identified and monitored polar lows by visual analysis of visible and thermal infrared imagery from satellites. However, this manual inspection method is subjective, time-consuming and inevitably involves errors in polar low detections. In this study, we present an automatic objective procedure which we demonstrate by detecting polar lows using spaceborne active synthetic aperture radar (SAR) and passive microwave radiometer observations. Based on the marker-controlled watershed segmentation method and the morphological image thinning algorithm, the centre locations of polar lows are determined using RADARSAT-2 and Sentinel-1A high-resolution SAR images and total atmospheric water vapour content fields from radiometers (AMSR2, SSM/I, GMI, and WindSat). Furthermore, the trajectories of polar lows are constructed, using detected centres from multi-temporal SAR and radiometer observations. Polar low detections are confirmed by high surface wind speeds from SAR, scatterometer, and radiometer data, the presence of cloud vortex signatures visible in MODIS, AVHRR, and VIIRS thermal infrared imagery, as well as the difference between the sea surface temperature and the air temperature at 500 hPa. These results show that the proposed methods have potential to automatically detect and track polar lows from multisensor data. We also estimate the characteristic parameters of detected polar lows. The diameters, translation speeds, and distances travelled are 189 km and 225 km, 8 m/s and 4.9 m/s, and 318 km and 263 km, respectively. [ABSTRACT FROM AUTHOR]

To make biologically useful interpretations of the echo signals from an entomological radar (a radar designed and operated to observe insects in flight), it is necessary to relate basic properties of insects (i.e. their mass, size, and form) to their radio-wave scattering characters – e.g. their radar cross-section (RCS) and its variation with aspect or beam polarization. Measurements of RCSs of live or freshly dead specimens in laboratory rigs have played an important role in establishing such relationships, but it is desirable to develop and validate methods employing numerical computation, using electromagnetic calculation software, as a more widely applicable and generalizable alternative. In such computations, the insect is represented by a model with specified size, form, and composition; composition appears as the complex relative permittivity, with both polarizability (real) and loss (imaginary) terms. As a preliminary to investigating how adequately different types of electromagnetic model perform, we have collected and analysed morphometric data on adult, flight-capable specimens of a wide range of insect species, including many known to be migratory and to fly at heights where they would be detectable by radar. Our analyses reveal that appropriate densities for prolate-spheroid models of flying insects are in the range 0.60–0.76 g cm−3, which is about half that measured for air-free samples of the solids and liquids from which insect bodies are formed. From this, appropriate permittivities for the material filling the model are found to be in the range 7–11 (real component) and 2.0–3.5 (imaginary); again, these are much lower than measured values for pure insect-body material. These results enable construction of simple electromagnetic models of insects in which both spatial dimensions and masses are accurate. Computation of model RCSs and their aspect and polarization variations, and comparison of these with laboratory measurements, will be the topic of a follow-up validation study. [ABSTRACT FROM AUTHOR]

In this study, a machine learning approach is applied to wind speed retrieval from vertical-vertical (VV) polarized synthetic aperture radar (SAR) during tropical cyclones (TC). More than 2400 dual-polarized (VV and vertical-horizontal (VH)) Sentinel-1 (S-1) images acquired in interferometric wide (IW) and extra wide (EW) mode are collected in 2016–2022. Along-track observations from stepped-frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) are available for several images. The azimuthal cut-off wavelength (ACW) is derived from VV-polarized image and the Doppler centroid anomaly (DCA) is provided from Ocean (OCN) product. It is found that SFMR wind speed linearly relates with ACW and DCA. Following this rationale, a machine learning method, called eXtreme Gradient Boosting (XGBoost), is trained for constructing SAR wind speed retrieval geophysical model function (GMF) through abundant matchups from 2000 images collocated with wind speeds from soil moisture active passive (SMAP) microwave radiometer and SFMR, denoted as TCWIND2-S1. Then GMF TCWIND2-S1 is applied for more than 400 images and the validation against SMAP and SFMR products up to 70 m s−1 shows a 3.01 m s−1 root mean squared error (RMSE) with a 0.16 scatter index (SI) and a 0.92 correlation (COR). Retrieval is also compared with CyclObs wind products derived from more than 400 dual-polarized images, yielding a 2.66 m s−1 RMSE with a 0.19 SI and a 0.93 COR. Our work provides an approach for VV-polarized SAR wind retrieval at wind speed ranged from 0 to 70 m s−1 during TCs. This work is conveniently adopted for TerraSAR-X without operating at cross polarization channel. [ABSTRACT FROM AUTHOR]

Radar signatures of rain over the ocean have a complex structure since they receive contributions from surface scattering and volume scattering and attenuation by hydrometeors in the atmosphere. These contributions overlap and are often difficult to detangle. While most of the mechanisms contributing to radar signatures of rain over the ocean are well understood, there is one remaining issue that has been discussed controversially in the literature for a long time. It is the question what scattering mechanism causes the areas of strongly enhanced radar backscatter, also called 'bright blobs' or 'bright patches', which are frequently observed on spaceborne C-band SAR images acquired over tropical and subtropical oceans in the presence of convective rain. Recently, papers have been published in which it is hypothesized that they are caused by radar backscattering at hydrometeors in the melting layer (ML). Although many observational facts seem to support this hypothesis, there exists one strong argument against this hypothesis: It is the observation that the position of the ML radar signatures (bright blob) in the SAR image is not shifted in anti-range from the position, where the rain column hits the sea surface. This absence of a shift is observed when 1) comparing Sentinel-1 SAR images on which rain cells are visible with quasi-concurrently acquired weather radar images and 2) when inter-comparing of SAR images of rain cells acquired concurrently at different frequencies and polarizations. Based on these observations, we discard the hypothesis that the bright blobs are due to volume scattering at hydrometeors in the ML and hypothesize instead that they are due to scattering at splash products at the sea surface. This hypothesis is supported by radar backscattering measurements carried out in the laboratory and from a shore-based platform, which show that, at C-and X-band, strong rain can give rise to strong radar returns also at cross-polarization. [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]

The Radar Cross Section (RCS) plays a crucial role in radar systems, serving to detect and characterize radar targets. Moreover, the need to penetrate foliage and capture images of man-made structures like roads and buildings has driven the development of very high frequency/ultra high frequency (VHF/UHF)-based radar systems. In this context, we propose a novel polyphase-coded spread spectrum (PCSS) radar system designed for RCS measurements and ISAR imaging over VHF/UHF radio channels in open environments. To provide a comprehensive understanding, we examined the open range, considering factors such as amplitude distribution function, root mean square (RMS) delay, and coherence bandwidth, followed by a link budget analysis. We also conducted a comparative study of RCS measurements to evaluate the performance of the proposed radar system, demonstrating its effectiveness through theoretical calculations. Furthermore, we assessed the radar imaging capabilities by applying the CLEAN algorithm to improve the quality of the generated images. [ABSTRACT FROM AUTHOR]

Based on particle swarm optimization-back propagation (PSO-BP) neural network, a novel inverse synthetic aperture radar (ISAR) image quality assessment (IQA) method is proposed to address the challenge of selecting high-quality ISAR images from a large number of images. This method incorporates six assessment metrics: energy gradient (EG), effective area ratio of image (EARI), image contrast (IC), image entropy (IE), equivalent number of looks (ENL), and signal-to-noise ratio (SNR) to evaluate the quality of ISAR images. The PSO-BP neural network is employed to fit the relationship between these metrics and ISAR image quality. Experimental results show that the new method is effective in ISAR-IQA under different imaging conditions and various attitudes, and closely matches the subjective evaluation of experts. [ABSTRACT FROM AUTHOR]

In this paper, the shadow statistical method, which has the merit of without calibration, is further investigated for retrieving significant wave height (SWH) based on shadowing from X-band marine radar images. For the shadow statistical method, the Smith fitting function (SFF) is utilized to fit the illumination probability obtained from the radar image and calculate the SWH. By deeply investigating the estimation of wave steepness from the shadow image, it is found that the analytic solution of wave steepness is not obtained, due to the complementary error function and exponential term in the SFF. Meanwhile, several screening processes of gradually discarding illumination probability are required to obtain accurate root mean square (RMS) wave steepness, which makes the calculation time-consuming. To solve these problems, both the complementary error function and exponential term in the SFF are approximated under the working conditions of marine radar in practice, and the analytic solution of the wave steepness is described with the illumination probability. The shore-based X-band radar images at Pingtan station from November 9 to 17, 2014 are collected to validate the validity of the proposed method. By using 254 sets of radar data with the SWH range of 0.5 ~ 3.5 m, the experimental results demonstrate that the correlation coefficient (CC) of the estimated SWH between the proposed method and the traditional method is 0.996, and the root mean square error (RMSE) is 0.034. The average running time is significantly reduced from 2.76 s to 0.44 s. [ABSTRACT FROM AUTHOR]

With the advancement of spaceborne synthetic aperture radar (SAR) resolution, the conventional stop-and-go approximation-based motion model is no longer suitable for high-resolution imaging algorithms. This paper conducts a comprehensive analysis of the approximation errors resulting from the stop-and-go approximation and proposes corresponding compensation methods. Firstly, an accurate echo model based on continuous platform motion is derived from the SAR geometry configuration. The impact of these errors on range and azimuth imaging is analysed from a spectral perspective, and the applicable boundary conditions are examined. Subsequently, compensation methods are proposed to eliminate the introduced approximation errors, addressing both"fast time" and"slow time" effects, thus enabling high-resolution imaging through improvements in traditional imaging algorithms. Experimental simulations validate the effectiveness of the error analysis and compensation methods, using X-band low Earth orbit SAR as an illustrative example. The proposed methods are not only compatible with existing mature imaging algorithms, but the analytical approach and compensation methods are also applicable to SAR systems operating on different carrier frequencies, orbits, and platforms. [ABSTRACT FROM AUTHOR]

Floods are among the most dangerous natural disasters, and their frequency and severity are rapidly increasing due to climate change. Flood inundation mapping is critical for better understanding which areas are prone to flooding and identifying emerging floods. Recently, deep learning algorithms have been developed to improve the accuracy and timeliness of the production of these maps. In this study a new architecture, named Explainable Flood Inundation Mapping Network (XFIMNet) is presented, which is an explainable deep learning segmentation model for producing flood inundation maps from both synthetic aperture radar (SAR) and multi-spectral optical images. Comparative analysis with state-of-the-art (SOTA) models is performed, where the versatility of the model is demonstrated, in that it achieves superior performance across both image types, while the SOTA models are less consistent. The models are further evaluated with only the red, green, and blue (RGB) bands of the Sentinel-2 images, demonstrating that the full 13-band image is the superior choice for training a deep learning model. XFIMNet achieves an Intersection Over Union (IOU) of 0.59 with SAR images, 0.7 with optical, and 0.47 with the RGB bands, outperforming each of the SOTA models, as well as providing the most detailed and accurate visual segmentation mask, with minimal increase in the computation time for training or inference. The findings of this study highlight that XFIMNet is not only able to provide the most accurate segmentation mask but also prioritizes the most appropriate features when making decisions across both image types. [ABSTRACT FROM AUTHOR]

Terrestrial radar interferometry (TRI) provides accurate observations of displacements in the line-of-sight (LOS) direction and is therefore used in various monitoring applications. However, relating these displacements directly to the 3d world is challenging due to the particular imaging process. To address this, the radar results are projected onto a 3d model of the monitored area, requiring georeferencing of the 3d model and radar observation. However, georeferencing relies on manual alignment and resource-intensive on-site measurements. Challenges arise from the significant disparity in spatial resolution between radar images and 3d models, the absence of identifiable common natural features and the relationship between image and spatial coordinates depending on the topography and instrument pose. Herein, we propose a method for data-driven, automatic and precise georeferencing of TRI images without the need for manual interaction or in situ installations. Our approach (i) uses the radar amplitudes from the TRI images and the angle of incidence based on the 3d point cloud to identify matching features in the datasets, (ii) estimates the best-fitting transformation parameters using Kernel Density Correlation (KDC) and (iii) requires only rough initial approximations of the radar instrument's pose. Additionally, we present the correct relation between cross-range and azimuth for ground-based radar instruments. We demonstrate the application on a geomonitoring case using TRI data and a point cloud of a large rock cliff. The results show that the positions of the radar image can be localized in the monitored 3d space with a precision of a few metres at distances of over 1 k m. This is an improvement of almost one order of magnitude compared to what had been achieved using standard approaches and direct observation of the radar instrument's pose. The proposed method thus contributes to the automation of TRI data processing and improved localization of small-scale deformation areas detected in radar images. [ABSTRACT FROM AUTHOR]

In this study, a new method combining wave-number energy spectrum (WES) and Genetic algorithm-back propagation neural network (GA-BPNN) is proposed to retrieve the rainfall intensity level from rain-contaminated X-band marine radar image. Since the intensity of spatial rainfall can be reflected by the distribution of energy in the wavenumber frequency domain, the obtained WES is divided into three wavenumber segments (low, medium and high wavenumber segments), and the ratio of the wavenumber in each wavenumber segment to the total wavenumber is calculated separately as the characteristic parameters. Based on the excellent network convergence speed and data prediction accuracy of the GA-BPNN, these calculated parameters are input into the constructed GA-BPNN for training to complete the task of rainfall intensity level retrieval. The proposed method is tested using data collected at the ocean observation station of Haitan Island in Pingtan County. Referring to the actual rainfall intensity synchronously recorded by the rain gauge, the retrieval accuracy of the proposed method is 97.4%, which is 4.3% higher than that of back propagation neural network (BPNN) not optimized by Genetic algorithm (GA). In addition, compared with the retrieval performance of the ratio of zero intensity to echo (RZE) method based on the occlusion area of radar image, the retrieval accuracy of the proposed method is improved by about 12.9%. [ABSTRACT FROM AUTHOR]

The Sissingué Gold Project is in Northern Côte d'Ivoire and part of the southern extension, a large peneplain of the landscape in South Mali. The area has experienced extensive weathering and erosion over a long time, resulting in a widespread and complex regolith cover. This diverse and vast regolith cover poses considerable difficulties for exploration. To overcome this problem, we adopted mapping of regolith units using machine learning (ML) with support vector machine (SVM) and artificial neural network (ANN) algorithms. The main objectives were to (1) make the predictive regolith map of the study region using these algorithms with data obtained from Landsat-8 operational land imager, Advanced Land Observing Satellite Phased Array Type L-Band Synthetic Aperture Radar digital elevation model and Sentinel-1A; (2) show the method of pre-processing and processing the required data and (3) develop the regolith landform unit (RLU) map from the predictive regolith map and collected data based on the Relict – Erosional – Depositional model. Tests using the SVM and ANN algorithms showed that both ML tools could accurately map regolith landforms. An innovative method of optimizing data using parameters is presented. The results showed that ANN outperformed SVM with an overall accuracy of 87.01% and a kappa coefficient of 0.84, whereas the corresponding values for SVM were 86.69% and 0.84, respectively. However, the validation data obtained from SVM-based prediction exhibited a better score than that of validation data obtained from ANN-based prediction. The Sentinel-1A radar band combined with Landsat-8 data reduced the vegetation-masking effect and improved the classification results. The RLUs of this area are composed primarily of relicts at 24.91%, including lateritic residuum and soil, depositional material at 36.39% with exotic sediments and ferrierite and the remaining 38.7% of erosional material made of saprolite, colluvial fragments, and mottled zones on flanks. [ABSTRACT FROM AUTHOR]

During the last decades, Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been vastly utilized in the military services and civil studies. Because of the sensibility of the SAR images of the imaging in the azimuth dimension, using the multi-aspect SAR image sequence is more practical than the single-aspect SAR image to achieve the superior classification accuracy in an actual SAR ATR task. At present time, multi-aspect SAR ATR models mostly utilize Recurrent Neural Networks (RNN) that depend on the sequence between samples and so suffer from the lack of information. In a practical work, a huge amount of training set is needed to train a deep learning network accurately, but it is so costly work to extract multi-aspect SAR images. So, in this article, a new model of multi-aspect SAR ATR is proposed by using a self-attention model. The proposed self-attention model is utilized to calculate the internal correlation between the original SAR images. At the same time, to develop the anti-noise capability of the proposed model and decrease the dependency on a huge amount of training data, a Convolutional Auto Encoder (CAE) is designed and utilized in the feature extraction section of the proposed model. On the other hand, unlike existing traditional methods, in this work we use both amplitude and phase information of SAR images to devolve the training process of the proposed model. It should be noted that all of the parameters of the proposed network are developed to a complex domain. In addition a complex backpropagation algorithm by using the gradient based model is used for training the network. In the end, experiments is obtained by using two MSTAR data set (the MSTAR-SOC and EOC). The experimental results prove that the proposed model not only can achieve a high recognitions rate on the case of sufficient training sample but also can obtain an acceptable rate in the case of small training samples in different complex situations. In addition, the simulation results demonstrate that by using the encoder of CAE in the proposed model, the whole configuration of the proposed model achieves the anti-noise capability that is a valuable benefit of any practical SAR ATR task. [ABSTRACT FROM AUTHOR]

With the continuous improvement of radar resolution, distributed characteristics are presented with regard to maritime moving targets, which occupy multi-resolution cells in Synthetic Aperture Radar (SAR) image domain. However, the current constant false alarm rate (CFAR) detection algorithms rarely consider the impact of distributed characteristics on target detection performance, and thus the corresponding performance evaluation methods could not be applicable to this issue. In this paper, a multi-station fusion detection method for maritime moving target (MMT) is presented based on three-dimensional (3D) sliding window. Firstly, the multi-station echoes in Cartesian-Doppler frequency rate (DFR) domain are obtained under the simple-transmitting and multiple-receiving operation configuration, and the 3D sliding window is designed to achieve the optimal matching for a specified target with any moving direction in terms of its prior information, i.e. Radar Cross Section (RCS), radar resolution and target size. Then, the target cells within the sliding window are directly processed by means of the M/N criterion, and thus to avoid target detection performance loss caused by the artificial construction of extended detection statistics. Finally, a novel quantitative evaluation method in regards to a high-resolution radar is designed by mining the relationship between the target detection performance and the occupied cell number, which could greatly improve detection probability of distributed targets on the premise of a constant false alarm rate. The proposed algorithm does not need to design complex extended detection statistics, which provides a feasible way for robust detection and performance evaluation for a high-resolution radar. Simulation results verify the effectiveness of this research. [ABSTRACT FROM AUTHOR]

Automatic despeckling from synthetic aperture radar (SAR) imagery plays a significant role in many urban applications. Recently, owing to the impressive performance of deep learning, various SAR despeckling methods based on the convolutional neural network (CNN) have been proposed for optical SAR images. However, existing CNN-based despeckling methods and spatial-domain-based methods exhibit certain limitations. The introduction of prior knowledge is not considered in the designing of classical denoising and certain CNN-based methods, thus leading to the loss of edge information and artefacts in the results. To address this issue, an a priori-based deep neural network SAR image despeckling method (SAR-PBDNN) was proposed. In this approach, the Bayesian formula is modelled to calculate the objective function suitable for the despeckling problem and is optimized using the alternating direction method of multipliers algorithm and deep learning method. The introduction of prior information effectively improved the denoising effect of SAR images. The SAR-PBDNN was evaluated using both simulated and real SAR images. The comparisons demonstrate the effectiveness of the proposed method. We conclude that the SAR-PBDNN has the potential to automatically despeckle SAR images with an accuracy that renders it a useful tool for practical application scenarios. [ABSTRACT FROM AUTHOR]

Estimating the velocity of electromagnetic waves in the medium is essential for ground penetrating radar (GPR) to reconstruct target scenes. However, the existing velocity estimation methods based on imaging algorithms, e.g. the back-projection (BP)- and the Stolt migration-based methods, are susceptible to clutter and cannot strike a balance between depth independence and computational efficiency. Therefore, a velocity estimation method is proposed based on the vertex extraction-guided local BP algorithm. Initially, the high-order moment (HOM) is introduced into GPR data by analysing the distributions of target hyperbolas and clutter. Designed to avoid clutter, the proposed HOM sliding window extracts the hyperbola vertex at a high signal-to-clutter ratio region. By virtue of the extracted vertex, the local hyperbola region is subsequently extracted to reduce computation. Additionally, this paper adopts the depth-independent BP algorithm and further develops a local BP algorithm utilizing its imaging flexibility. Guided by the extracted vertex, the proposed local BP algorithm is iteratively performed on the local hyperbola region to estimate the velocity by a focus metric. Comprehensive experiments demonstrate the effectiveness and robustness of the proposed velocity estimation method. The proposed method outperforms the existing methods in clutter robustness, computational efficiency, and depth independence. [ABSTRACT FROM AUTHOR]

Very-high frequency (VHF) radar echoes are known to be aspect sensitive in general, which could be either the manifestation of a thin stable layer providing sharp refractive index gradient or shear-driven steep layer structures. The present study for the first time shows the volume imaging of aspect sensitivity in the recently upgraded Advanced Indian Mesosphere Stratosphere Troposphere (MST) radar (AIR). The present configuration of radar can provide a better understanding of three-dimensional structures of turbulence and instabilities, which was not possible when the radar was operated in two orthogonal planes. Both isotropic and anisotropic layers/turbulence are observed at various height levels over the radar volume. Enhanced aspect sensitivity is observed between 6 and 9 km, which is attributed to the presence of dynamic instability arising due to strong wind shear and atmospheric stability. All the scatterers tend to become isotropic when both the square of wind shear and stability parameters are above 0.25 × 10−3 s−2. Due to the high aspect sensitivity, there may be an underestimate of 5–10% in wind velocity measurements, and it is higher for the low off-zenith beam angle. [ABSTRACT FROM AUTHOR]

Aeolus is European Space Agency's unique and novel wind measuring satellite mission providing near real-time wind profiles from near surface to an altitude of 30 km. This paper presents the validation of Aeolus wind profiles over Cochin (10.04° N, 76.9°E), India using the 205 MHz wind profile radar. The Aeolus wind profiles (baselines 10 and 11) have been validated for an altitude range of 1 to 18 km during June, 2019 to September, 2021. Aeolus Rayleigh wind for clear ( R a y l e i g h c l e a r ) and Mie wind for cloudy ( M i e c l o u d y ) show very good agreement with the radar wind profiles. The Pearson correlation coefficient between radar and Aeolus wind for R a y l e i g h c l e a r and M i e c l o u d y are 0.93 and 0.94, respectively. The systematic and random errors in R a y l e i g h c l e a r are found to be −0.15 m s − 1 and 4.87 m s − 1 , respectively, while these values are −0.06 m s − 1 and 3.68 m s − 1 for M i e c l o u d y . A detailed error characterization of Aeolus wind profiles with respect to radar is presented in this study. The bias in Aeolus wind is provided in terms of observation altitude, seasons, different windy conditions and ascending/descending orbits. [ABSTRACT FROM AUTHOR]

Detecting the field maturity moment for maize (Zea mays L.) crop represents a relevant point to estimate its optimal harvest time. Knowing the optimal harvest time (defined by grain moisture content) at the end of the crop season is a major concern for maize farmers, as it could lead to substantial economic losses if not harvested on time. For this crop, optimal harvest time usually occurs 3–4 weeks after field maturity, depending on weather conditions. Therefore, this study focused on the interferometric coherence time-series analysis at the end of the maize crop season, to indirectly estimate the field maturity. For such purpose, a coherence object-based change detection method using Sentinel-1 SAR images was developed aiming to estimate the potential field maturity time. These estimations were assessed using an independent data set of field maturity dates obtained through field inspection and crop growth modelling. The technique was tested over 52 fields in the northwest region of Kansas, United States, with a detection rate of 80%, and a field maturity estimation error of 10 days (assessed with the root mean square error). The proposed method constitutes a promising approach to estimating the maize field maturity in near-real time, determining the field harvest readiness, and developing a decision support tool to assist farmers in prioritizing the allocation of fields at harvest time. [ABSTRACT FROM AUTHOR]

Distributed Inverse Synthetic Aperture Radar (ISAR) systems employ multiple spatially separated radars to observe a target, with the potential to enhance radar imaging azimuth resolution and gather more target information. Due to the existence of gaps in the observation angles of each radars, the coherence between pulses of radar echo is disrupted during fusion imaging, making it challenging for the Range-Doppler (RD) algorithm to achieve well-focused images. This paper proposes an imaging method based on sparse signal recovery (SSR), using the reweighted ${l_1}$ l 1 norm with convolution and total variation as regularization terms. This approach not only yields well-focused ISAR images but also better preserves the overall target information compared to other methods, while also exhibiting excellent noise resistance. In addition, the alternating direction method of multipliers (ADMM) algorithm is utilized for the solution to reduce computational complexity. Simulation and measured data verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Acquiring precise change map for synthetic aperture radar (SAR) images poses challenges due to the absence of labelled datasets and the presence of speckle noise interference. This paper presents a two-stage fusion strategy coupled with capsule network classification detection method. Initially, to enhance the structure of change areas within the difference image (DI), we propose an energy ratio (ER) algorithm based on the mean ratio (MR). Subsequently, employing a two-stage fusion strategy integrating multiplication and discrete wavelet transform (DWT), we fuse the DIs generated by the three algorithms to effectively complement change information across distinct DIs. In addition, a saliency detection algorithm is introduced to mitigate noise interference and balance the number of samples. Hierarchical fuzzy c-means clustering (HFCM) is then used to pre-classify the salient fusion difference image (SFDI) and derive pseudo-labels. Finally, we apply the residual shrinkage capsule network (RSCN) to reclassify pre-classification results and obtain the change detection outcome, capable of extracting the multi-scale refinement features and adaptively learning discriminant information from changed and unchanged regions. Experimental evaluations on various SAR image datasets demonstrate the exceptional detection performance of the proposed method. [ABSTRACT FROM AUTHOR]

Ice cover on subarctic lakes is an important indicator of climate change at local- or regional scales. This study proposes a new approach for classifying altimetry data from Jason-2 and SARAL/Altika satellite missions to characterize surface states of subarctic lakes. It focuses on Great Slave Lake (Canada) during freeze-up and thaw periods. For the first time, parameters from altimetry waveforms were used in an unsupervised clustering to establish distinct clusters from waveforms observed from Jason-2 and SARAL/Altika during the freeze-up and the thaw period. Clusters are assigned to the different surface states (open water, pure ice and leads) based on a priori altimetry and radiometric information. The statistics of these clusters were then used to construct two trained models of supervised classification based upon KNN (K-nearest neighbour) and SVM (support vector machine). The SVM-based model yielded the best results (accuracy of 92% with Jason-2, and 98% with SARAL/Altika). It was used to classify all waveforms considered in the study from the nominal orbits of Jason-2 (2008–2016) and SARAL/Altika (2013–2016). Results were superimposed onto Moderate Resolution Imaging Spectroradiometer (MODIS) products for qualitative visual and semi-quantitative assessments. [ABSTRACT FROM AUTHOR]

The outbreaks of green tide have caused severe harm to the marine environment and human society. Synthetic Aperture Radar (SAR) plays an important role in green tide monitoring by virtue of its high resolution and cloud-free nature. The existing green tide extraction methods still face challenges in identifying multi-scale green tide patches due to noise interference, uneven greyscale and blurred boundaries in SAR images. Meanwhile, the practical application of deep learning methods with high precision is limited due to the complexity of the model and the large amount of computation. Therefore, we propose a multi-scale context-aware and batch-independent lightweight green tide extraction network called MBL-Net. A novel lightweight heterogeneous backbone is designed to extract multi-scale discriminative features and improve segmentation efficiency by using multi-scale selection kernel (MSK) modules and lightweight stages. Meanwhile, Triplet attention module is introduced to improve the internal consistency of the green tide region and suppress the effect of speckle noise. Then, the mixed pooling-based channel prior module (MCPM) is used to expand the receptive field of the network and extract the fine green tide structure by fusing multi-scale features. In addition, Filter Response Normalisation (FRN) is innovatively applied for feature normalization in the decoding stage, eliminating batch dependency. In order to verify the effectiveness of the proposed method, a dataset is built using the Sentinel-1 images of the Yellow Sea, China, from 2019 to 2021. The experimental results show that the proposed method achieves an overall accuracy of 98.59% with 0.970 G FLOPs and 3.525 M parameters, which ensures high precision and improves green tide detection efficiency. Compared with several representative networks, this method can capture more details of green tide with fewer parameters and faster calculation speed. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is an important clutter suppression technique in airborne multichannel radar. It can be a challenging problem to apply STAP to the maritime airborne radar without knowledge of sea clutter space-time coupling characteristics. The radar sea clutter data with space-time coupling characteristics can be obtained by airborne multichannel radar, but the flight testing is very expensive. A cost-effective technique is presented for emulating airborne motion based on shore-based multichannel radar. The technique is implemented by the same array shared with transmit and receive antenna, where the transmit array uses an inverse displaced phase centre antenna to emulate platform motion and the receive array is used for receiving data. Based on the above technique, a sea clutter model of aircraft motion emulation is proposed. Then, we verify the model by quantitatively comparing the number of eigenvalue and the spread width of the space-time power spectrum between the simulated and measured data. The result shows that the eigenvalue number and the spread width of 3 dB clutter ridge of measured data are more than 79.5% of that belonging to simulated data. It means that the simulated radar sea clutter data generated by this model can better fit the measured data under sea states classes 1–4, which can provide support for the analysis of sea clutter space-time characteristics. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) came into practical use as a geohazard monitoring tool in the 1990s. The uptake of this remote sensing technique however is geographically varying, and Australia has been one of many continents not fully utilizing the available InSAR data. As a result, limited knowledge exists on the application of InSAR data to obtain insights and benefits in geohazard risk monitoring and management. Due to the said limitation, this research carried out a concise technical background study and investigated a suite of case studies to demonstrate the potential for geohazard monitoring and assessment in Australia using InSAR. The differential InSAR workflow was applied to sites that experienced heaving and subsiding of the ground surface, mining, earthquake, landslides, and tunnelling in West Metro Melbourne (VIC), Musgrave (SA), Crinum mine (QLD), Pelham (TAS), Lake Muir (WA), and Sydney (NSW). The results of the Differential InSAR analysis resulted in acceptable ground movement and deformation estimates when compared with measured field data. Future opportunities related to new applications of the InSAR technique on geohazard reduction considering climate change contributions and probable coupling with artificial intelligence techniques were also discussed. This research has formed a conducive and scientific learning medium, which may encourage the growth of multidisciplinary research combining the fields of remote sensing and geotechnical engineering to investigate the impact of geohazards. [ABSTRACT FROM AUTHOR]

Empirical wind-retrieval algorithms for synthetic aperture radar (SAR), such as the geophysical model function (GMF) CMOD5N in the C-Band, are currently well developed. The retrieval accuracy of GMFs, however, is lower in the presence of marine phenomena such as oceanic fronts and mesoscale eddies, making SAR wind retrieval challenging under these conditions. In this study, we proposed a scheme for wind retrieval from Chinese Gaofen-3 (GF-3) SAR images using an analytical approach to obtain the wind speed in the nearshore waters of China's seas. Approximately 300 images were acquired from 2018 to 2021 in the copolarization channels [vertical–vertical (VV) and horizontal–horizontal (HH)]. The images were collocated with the measurements from the Advanced Scatterometer (ASCAT) and the Chinese Haiyang-2B (HY-2B) scatterometer. Our analytical approach was based on the Bragg resonant and non-Bragg (NB) scattering model for the calculation of the normalized radar backscatter cross-section (NRCS), which accounted for the influence of wave-current interactions and breaking waves in nearshore waters. We used the sea surface currents obtained from the HYbrid Coordinate Ocean Model (HYCOM) to simulate the Bragg resonant roughness. We derived the contribution of the NB breaking waves from the SAR-measured NRCS values in both the VV and HH channels. By comparing the retrieval results obtained using the analytical approach with the scatterometer products, we concluded that the root mean square error (RMSE) of the wind speed was 2.01 m s−1 with a 0.91 correlation coefficient (COR), which was less than the 2.93 m s−1 RMSE and 0.72 COR for the GMF CMOD5N. The variation in the bias of the HYCOM currents was approximately 1 m s−1 for current speeds greater than 0.3 m s−1. These findings verified the stability of the analytical approach for SAR wind retrieval in complex sea states. [ABSTRACT FROM AUTHOR]

L-band spaceborne polarimetric synthetic aperture radar (PolSAR) data has been well used for the high-resolution retrieval of the ionospheric Faraday rotation (FR). A vast majority of these retrievals were, however, conducted on PolSAR data of lands, with only a very few over oceans. For the global imaging of the Earth's ionosphere, the retrieval of oceanic ionosphere is indispensable. This paper aims to furnish a deep insight into the FR retrieval on oceans using the widely used Bickel-Bates estimator based on the two cross-circular polarizations ${Z_{12}}$ Z 12 and ${Z_{21}}$ Z 21 . The FR estimates (FRE) on oceans are evaluated in detail by comparing with those on lands on four ALOS PALSAR datasets. The achieved oceanic FRE are shown to possess comparable or even better quality than those on lands. It occurs in three of the four scenes that the selected ocean sub-area holds a smaller standard deviation (STD) of FRE (less than 0.7000°) than all the selected land sub-areas. Especially, the quality of FRE is shown to be closely linked to the dispersed degree of the magnitude of the conjugate product of ${Z_{12}}$ Z 12 and ${Z_{21}}$ Z 21 via the analysis of coefficient of variation (CV), according to which the unusual occurrence of larger magnitude level corresponding to larger STD of FRE can be explained. Nevertheless, compared with CV, the magnitude of the polarimetric coherence between ${Z_{12}}$ Z 12 and ${Z_{21}}$ Z 21 (i.e. $\left| \gamma \right|$ γ ) is identified as a better index to measure the quality of FRE. A smaller STD of FRE appears along with a larger $\left| \gamma \right|$ γ . Three of the four scenes show that the selected ocean sub-area bears a larger mean of $\left| \gamma \right|$ γ (greater than 0.9890) in comparison with all the selected land sub-areas. This work can promote the ionospheric research over oceans and to the correction of ionospheric effects in oceanic L-band spaceborne polarimetric radar data as well. [ABSTRACT FROM AUTHOR]

In inverse synthetic aperture radar (ISAR) imaging, the conventional range-Doppler (RD) algorithm cannot obtain satisfactory imaging results for sparse aperture. Compressed sensing (CS) imaging methods, such as the typical iterative shrinkage and thresholding algorithm (ISTA), are often used in sparse aperture radar imaging. However, CS-based methods have high computational complexity and difficulty in setting parameters. To overcome the shortcomings, a new deep unfolding network named complex-valued weighted learning ISTA (CV-WLISTA) is proposed in this paper. The new network is very efficient and can learn the parameters adaptively. Based on ISTA and a new weighting strategy, it takes advantage of both the sparsity and the structural property of ISAR images to improve imaging performance. The experimental results show that CV-WLISTA has better reconstruction performance and higher efficiency compared with the traditional algorithms. Therefore, CV-WLISTA is an efficient ISAR imaging method with excellent performance. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image change detection is a key technique for such essential applications as flood disaster assessment and forest fire detection. In SAR image change detection, clustering algorithm is the most applied methods, but clustering algorithm only considers the grey features, therefore it is more susceptible to speckle noise. Deep learning model is difficult to be trained by supervised learning because of lack of labels. To alleviate the above-mentioned challenges of SAR image change detection, a parallel dual-branch SAR image change detection network based on clustering and segmentation (Clustering-Segmentation Network) is proposed in this paper. In the clustering branch, the clustering-based change detection results were obtained by fuzzy c-means (FCM) clustering. In the segmentation branch, the Graph-Based Image Segmentation algorithm was used for pre-segmentation. These results were used as labels of the neural network for training for the segmentation-based change detection. After the fusion of these dual-branch, a double sparse dictionary (DSD) discrimination algorithm is proposed to extract the neighbourhood features for the final discrimination, and obtain the final results. By fusing the dual-branch results, the influence of speckle noise on change detection can be suppressed while maintaining a high accuracy. We show that the Clustering-Segmentation Network exhibited better results compared with existing algorithms on several datasets. The accuracy and kappa coefficients are improved by 0.83% and 3.45% respectively, thereby proving the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

A measurement setup based on a high-precision, low-cost FMCW radar sensor has been developed for radar-based 1D deformation monitoring of engineering structures. The method enables contactless and time-continuous scans, but has lower acquisition costs compared to commercially available terrestrial laser and radar technologies. The article contains the instrument-related description of the sensor with accuracy tests for 1D distance measurements on a laboratory scale as well as a case study for monitoring the movements of a headframe over the course of the day. The detectability of deformations was statistically verified by means of a hypothesis test and applied to the case study under real conditions. With a defined target object (corner reflector), measuring accuracies of up to 10 μm with a 99,7% confidence level could be achieved. The case study shows absolute movements of the headframe during the day of 1 mm. Changes of 0,2 mm can be detected in time intervals of 9 min with a statistical significance level of 95%. Abbreviations : CA-CFAR: Cell-averaging constant false alarm rate; CR-I, CR-II: Corner reflectors; CUT: Cell Under Test; DFT: Discrete Fourier transformation; EXP: Experiment; FMCW: Frequency-modulated continuous-wave; iSDR-C:indurad ScanningDynamicRadar-Compact; LOD: Limit of detection; PI: Peak interpolation [ABSTRACT FROM AUTHOR]

Crop type mapping visualizes the spatial distribution pattern and proportion of planting areas of different crop types, which is the basis for subsequent agricultural applications. Although optical remote sensing has been widely used to monitor crop dynamics, data are not always available due to cloud and other atmospheric effects on optical sensors. Satellite microwave systems such as Synthetic Aperture Radar (SAR) have all-time and all-weather advantages in monitoring ground and crop conditions, combining optical imagery and SAR imagery for crop type classification is of great significance. Our study mainly proposes seven feature combination schemes based on the combination of multi-temporal spectral features and texture features of Sentinel-2 (S2), and radar backscattering features of Sentinel-1 (S1) evaluate the influence of different data sources and different features on classification accuracy, obtains the optimal classification strategy and analyses the contribution of different features to classification result, in the aim of providing a new technical approach for the fine identification of crops from multi-source remote-sensing data. Results show that the crop classification accuracy of combined multi-time series spectral, texture, and radar features is higher than that of combining two types of features. The features subset selected by multi-period spectral, texture, and radar features have the best classification result, the overall accuracy (OA) and kappa coefficients reach 96.40% and 0.93, respectively. The study provides a method reference for future research on larger-scale remote-sensing crop precise extraction. [ABSTRACT FROM AUTHOR]

For spaceborne ISAR imaging of space targets, due to the high-speed relative motion between the radar and targets, both the translational and rotational motion exhibit the high-order characteristics. Conventional translational motion compensation (TMC) methods are ineffective in handling such high-order translational motion. Moreover, the migration through resolution cell (MTRC) stems from the presence of the high-order spatial variant phase of the echo signal, resulting in severe defocusing of ISAR images. In this paper, a novel spaceborne ISAR imaging approach for space targets is proposed, which incorporates the high-order TMC and the high-order MTRC correction. First, the slant range model for spaceborne ISAR imaging is derived as a third-order polynomial form. Based on this model, a modified high-order TMC method is proposed, which utilizes the minimum entropy method combined with the Broyden – Fletcher – Goldfarb – Shanno (BFGS) algorithm. The proposed method leverages orbital model information to obtain the initial TMC coefficients, enabling TMC to be completed more accurately and effectively. Then, a modified polynomial keystone transform (PKT) is proposed to correct the high-order MTRC. The PKT-based method effectively addresses both the high-order and first-order MTRC corrections simultaneously. Finally, a spaceborne ISAR imaging approach is proposed to obtain well-focused ISAR images. Simulated and real data results validate the analysis of the spaceborne ISAR imaging model and demonstrate the effectiveness of the spaceborne imaging approach for space targets proposed in this paper. [ABSTRACT FROM AUTHOR]

This study investigates the prediction of lightning flash rates using a ground-based, S-band polarimetric weather radar and storm cell-based lightning prediction. Both short-term cases during multiple years and long-term cases are used for training and testing by combining the radar and ground-based lightning detection network measurements. The identified lightning-generating storm cells are tracked during the data pre-processing to initiate our machine learning (ML) algorithm. The selection of features for ML algorithm implementation is discussed, and the performance of lightning rate prediction is evaluated using Pearson's correlation coefficient (r) and mean percentage error (MPE) metrics. The results show that using polarimetric measurement can improve prediction accuracy with short-term data sets, while for long-term datasets, the improvement is less significant. In general, reasonable prediction accuracy is achieved for up to 30 minutes of lead time. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR) images, inland water bodies generally appear as dark textures, but due to the interference of thermal noise, speckle noise, and azimuth blur, inland water bodies can be easily confused with weakly scattered areas such as roads, wetlands, and sand. Most of the existing denoising methods have been proposed for removing speckle noise. However, the presence of thermal noise and azimuth blur still makes it difficult to distinguish inland water bodies. The denoising method based on the Doppler spectrum can effectively remove thermal noise, speckle noise, and azimuth blur, but it reduces the resolution, which blurs the contour of the water body, and it is difficult to apply to the detection and processing of small inland water bodies. Fusing a noisy high-resolution image with a low-resolution image with less noise makes it possible to remove the speckle noise and thermal noise of the SAR image while maintaining the edge texture of the water body. Based on the idea of image fusion, sparse representation is used to fuse the original noisy SAR image (HR) and the low-precision image (LR) processed by the denoising algorithm to maximise the use of all the information in HR and LR. According to this idea, an enhancement algorithm for separability of inland water body based on sparse representation is proposed. The experimental results show that the proposed model significantly improves the separability of inland water bodies in SAR images. [ABSTRACT FROM AUTHOR]

As a basic scattering element, arc-shaped structures can present considerable geometric information of the observed targets in synthetic aperture radar (SAR) images. The arc-shaped structures of various artificial objects sometimes completely disappear in radar images, which dramatically degrades the applicability of SAR images. Conventionally, multi-aspect observation is required to recover the disappeared structures, but this method is not always effective in some cases. This paper proposes a parametric reconstruction of arc-shaped structures in SAR imaging. The proposed method has the ability to reconstruct disappeared arc-shaped structures via three observations. The core idea is to use the anisotropic phase feature to distinguish the endpoints of arc-shaped structures from multiple point-like targets in the image domain and restore the arc through the scattering model. The method basically includes three procedures: arc-shaped structure recognition, parameter estimation, and target reconstruction. Computer simulations and a microwave anechoic chamber experiment are conducted to examine the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Although the atmospheric compensation models developed to date have been generally robust and effective for InSAR, how to choose the right atmospheric correction method for ground-based InSAR is worth studying. This paper uses different methods for atmospheric phase screen (APS) compensation based on studies of ground-based radar (GBR) to zero-baseline acquisitions over the Woda landslide in Sichuan, China. This landslide has a steep topography, thick fog, turbulent rivers and strong rains. Data were acquired at the Ku-band using a GAMMA Portable Radar Interferometer II (GPRI-II) in multiple campaigns (4 July 2019–13 July 2019). In 2019, the Miyun open-pit mine was also studied; the maximum deformation reached 4.03 mm/hour. The collected data were processed within one unit based on the small baseline set (SBAS) approach. The experimental results show the following (1) For long-distance monitoring with complex atmospheric disturbances, range- and/or height-dependent models fail. (2) The iterative decomposition (ITD) method can effectively address complex atmospheric disturbances. (3) If the threshold of wavelength is set to be large, the ITD method becomes a stratified model. [ABSTRACT FROM AUTHOR]

High-precision observation of ocean surface topography is of great significance in studying the ocean mesoscale and sub-mesoscale processes. Spaceborne near-nadir interferometric synthetic aperture radar (InSAR) is capable of measuring sea surface height with centimetre-level accuracy and over a wide swath. It differs from terrestrial height measurement because the constant movement of the dynamic ocean surface creates biases in synthetic aperture radar imaging and leads to height measurement inaccuracy. This study investigates the bias due to the motion of ocean surface waves and establishes a theoretical model of the height measurement motion bias related to the ocean wave spectrums and InSAR system parameters. The motion bias model was simulated in different SAR modes, including Stripmap and Terrain Observation by Progressive Scans (TOPS) modes in various oceanic states. The results showed that the motion biases are smaller in Stripmap mode than TOPS mode under the same oceanic conditions. By adopting the total zero steering technology in Stripmap mode, the motion bias can be significantly decreased. In the same oceanic state, the motion bias increases with increasing angle of incidence. In the TOPS mode, the Doppler centroid frequency of the antenna's signal changes linearly with the azimuth time, and thus, the biases change significantly in the azimuth direction. The motion bias model is validated by the interferometric full-link experimental simulation and the results are consistent with the theoretical values. This study provides a compensation method with millimetre level accuracy to compensate for the height measurement bias caused by sea surface motion. The theoretical derivation of motion bias models and simulation results provide feasible suggestions for the design of InSAR systems and bias compensation for sea surface height measurement. [ABSTRACT FROM AUTHOR]

In previous research, the identification of the Bragg region, which is often achieved using image recognition, was easily affected by ionospheric interference and different manual parameter settings. Strong disturbances from the ionosphere and other environmental noise may interfere with high-frequency radar (HFR) systems when determining first-order Bragg regions and thus may directly influence the surface current mapping performance. To avoid human intervention in first-order Bragg-region recognition, deep learning methods were used to extract multiple levels of feature abstractions of the first-order Bragg region. In this study, to assist in developing sufficient training data, a procedure integrating a morphological approach and Otsu's method was proposed to provide manual labelled data for a U-Net deep learning model. Additionally, several sets of activation functions and optimizers were used to achieve optimal deep learning model performance. The best combination of model parameters results in an accuracy of over 90%, an F1 score of over 80%, and an intersection over union (IoU) reaching 60%. The identification time of one image is approximately 70 ms. These results demonstrate that this deep learning model can predict the position of first-order Bragg regions under ionospheric interference and avoid being affected by strong noise that could cause prediction errors. Hence, deep learning and image fusion processing can effectively recognize the first-order Bragg regions under strong interference and noise and can thereby improve the surface current mapping accuracy. [ABSTRACT FROM AUTHOR]

Ship detection in synthetic aperture radar (SAR) images is widely applied in marine monitoring. In recent years, convolutional neural networks (CNNs) have made significant advances in SAR ship detection. However, the scene imbalance problem, i.e. much more offshore scenes than inshore scenes in SAR images, severely hampers the improvement of detection accuracy. In this article, we propose scene-aware data augmentation to address the problem. First, a novel concept, inshore rate, is introduced to estimate the land area percentage of various scenes in SAR images. We can divide image samples into inshore and offshore ones based on the inshore rate without manual category annotations. Second, to augment inshore samples, we synthesize dummy inshore images by cropping the ground truth bounding boxes (GT bboxes) from offshore images and pasting them onto inshore images. This offline data augmentation strategy can address the scene imbalance problem and improve detection accuracy. The experiments conducted on SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset (LS-SSDD) demonstrate the effectiveness of the proposed method in improving detection performance. [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]

Oceanic internal waves play a crucial role in ocean activities. Currently, the approach to detecting oceanic internal waves from synthetic aperture radar (SAR) images is becoming robust. To efficiently identify the stripes of oceanic internal waves from SAR images, we propose an integrated algorithm for the detection and recognition of oceanic internal waves. First, the Gamma Map filtering method was adopted to reduce speckle noise in the SAR images. Then, histogram of orientated gradients (HOG), Grey level co-occurrence matrix (GLCM), and fractal dimension (FD) were utilized to extract the image features. Subsequently, support vector machine (SVM) was adopted to classify the SAR images and obtain images that contain oceanic internal waves. Next, the Canny edge detection method was used to detect and recognize the stripes of oceanic internal waves in the SAR images, and these stripes were screened by three parameters, namely their lengths, area ratios, and directions. Finally, the positions of the stripes of the oceanic internal waves were obtained. The experimental results verify that the proposed method can identify whether SAR images contain oceanic internal waves, and also determine the locations of their stripes in the SAR images. Meanwhile, the algorithm exhibits reasonable robustness and recognition rate. In addition, the optimal accuracy and kappa coefficient ( κ) are 94.2% and 0.878, respectively. [ABSTRACT FROM AUTHOR]

In the linear array synthetic aperture radar (LASAR) three-dimensional (3D) imaging, the spacing between adjacent elements in the uniform linear array (ULA) must satisfy the Nyquist sampling theorem to avoid the grating lobes, which makes the number of elements in the ULA very large. To reduce the elements in the ULA, the coprime adjacent array (CAA) with the same aperture length as the ULA is used when conducting LASAR 3D sparse imaging by compressed sensing (CS) algorithms. However, due to the increased autocorrelation coefficient of the measurement matrix, there exists grating lobes interference in the CAA-SAR imaging results. To solve this problem, we propose a joint sparse recovery (JSR) algorithm for CAA-SAR 3D sparse imaging. Firstly, we conduct sparse imaging on the CAA and its two subarrays, respectively. Secondly, the imaging results of the CAA and its two subarrays are performed image segmentation by the OTSU algorithm to extract their target-areas' imaging results. Finally, we perform the image fusion by the wavelet transform on the target-areas' imaging results to obtain the final imaging results. Both simulation and experimental results indicate that the imaging quality and computational efficiency of the JSR algorithm are higher than the random sampling array (RSA) and CAA under the same number of array elements. Besides, under the same aperture length, the JSR algorithm improves the computational efficiency than the ULA without imaging-quality loss. [ABSTRACT FROM AUTHOR]

Normalized Difference Vegetation Index (NDVI) time series data are used by agricultural agencies for many essential operational crop monitoring programmes. But optical sensors often miss key growth stages due to cloud cover interference, impacting the performance of operational activities. Although the synergistic use of optical and Synthetic Aperture Radar (SAR) imagery can provide time series data, any SAR-optical integration necessitates building a relationship between these two data sources. The objective of this study was to use a semi-empirical Canopy Structure Dynamics Model (CSDM), Growing Degree Days (GDD), and SAR parameters calibrated to optical NDVI to derive daily estimates of canola crop condition over an entire growing season. RADARSAT-2 Fine Quad-pol and RapidEye images were collected over three years for a study site in western Canada. Object-based image analysis was applied to study the relationship between the optical and SAR time series data. Significant correlations were documented between a number of SAR parameters and optical NDVI, specifically a ratio of backscatter intensities (HH-HV)/(HH+HV), a ratio of volume to surface scattering extracted from the Freeman Durden decomposition, and Entropy from the Cloude-Pottier decomposition. Correlations (r-values) between these SAR parameters and optical NDVI ranged from 0.63 to 0.84 for the three years of data. Based on this analysis, a simple statistical model was used to relate SAR parameters to optical NDVI, creating a SAR-calibrated NDVI (SARcal-NDVI). A CSDM was fit to the SARcal-NDVI for each canola field, constructing a temporal vegetation index curve which captured canopy development from emergence to senescence. Coefficients of determination (R2) were 0.87 0.86 and 0.82 for entropy, the volume-surface scattering ratio, and the ratio of backscatter intensities (HH-HV)/(HH+HV), respectively, demonstrating a good model fit. The CSDM describes well the temporal evolution of SARcal-NDVI. Using the CSDM, SARcal-NDVI and GDD, the canola condition can be estimated for any given day in the growing season. In fact when the CSDM was used to estimate SARcal-NDVI for the exact days of RapidEye acquisitions, correlations with optically derived NDVI were high. The strongest correlations with RapidEye NDVI were reported for the volume-surface scattering ratio (R2 of 0.69 and RMSE of 0.15). The SARcal-NDVI estimated from the CSDM was also physically meaningful. Field-based biomass was significantly correlated (R2 of 0.79) with the SARcal-NDVI calculated using the volume-surface scattering ratio. Although further research is needed to extend this method to other crops, these results demonstrate that SAR data can be used to estimate vegetation conditions and when coupled with a CSDM, integrated into current monitoring operations based on optical NDVI. As a next step, the research team will be assessing SARcal-NDVI in a national operational programme which reports on crop yields using modelling with optical-based NDVI. [ABSTRACT FROM AUTHOR]

Existing deep learning-based change detection methods in the field of polarimetric synthetic aperture radar (POLSAR) usually directly deal with intensity images. Methods can be easily transferred from optical image processing to synthetic aperture radar (SAR) image processing. However, the polarization information, which is critical in POLSAR data, has been discarded under normal situations. This paper introduces a novel joint change detection network based on similarity learning for coregistered complex-valued SAR data. A pseudo-siamese network takes both amplitude information and polarization information of POLSAR data as the input. The fusion of low-level, middle-level and high-level features enables the network to keep high-resolution and have strong representation ability during training procedures. Our novel sub-networks, which we term C2-Net and Intensity-Net, deal with the covariance matrix of complex SAR data and amplitude SAR data, respectively. The Intensity-Net works as a typical classification network and detects targets directly. The C2-Net attempts to find the relationship between two SAR data patches. An improved cosine similarity function is used to measure the similarity between two generated feature vectors in C2-Net. Output probability vectors of the two sub-networks are combined for final change detection. The two sub-networks are trained jointly and simultaneously. Control experiments show that proposed improvements are working. Experimental results on dual-polarization complex-valued SAR data from Sentinel-1 demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Seagrass provides numerous valuable ecosystem services across a wide range of climatic regions. However, in terms of area and habitat, this resource is in decline globally and there is an urgent need for accurate mapping of extant meadows and biomass to support sustainable seagrass blue carbon conservation and management. This study develops a novel method for a binary mapping of seagrass distribution and estimating seagrass above-ground biomass (AGB) by applying a suite of advanced machine learning (ML) algorithms combined with and without a metaheuristic optimization approach (particle swarm optimization – PSO) to various combinations of multispectral (Sentinel-2) and synthetic aperture radar (Sentinel-1) remote sensing data. Our results reveal that the Sentinel-1 data has potential for the binary mapping of seagrass meadows using an extreme gradient boosting (XGB) model (scores of precision (P) = 0.82, recall (R) = 0.90, and F1 = 0.86) but is less effective at estimating AGB. The optimal method for estimation of AGB used both Sentinel-1 and Sentinel-2 imagery, the XGB model, and PSO optimization (coefficient of determination (R2) = 0.75, root mean squared error (RMSE) = 0.35, Akaike information criteria (AIC) = 24.80, Bayesian information criteria (BIC) = 44.70). Our findings contribute novel and advanced methods for seagrass detection and improvement of AGB estimation, which are fast and reliable, use open-source data and software and should be easily applicable to intertidal zones across many regions of the world. [ABSTRACT FROM AUTHOR]

Snowpack in sloped mountain terrain may present stratification in which layers of snow accumulate over a certain period. The loosely adhering layers in snowpack may exhibit a severe avalanche risk, and detecting such stratification phenomena is key to avalanche detection. The danger of manual, in-situ sampling calls for investigation of alternative approaches, including the use of in-situ radars. Considering a buried radar sensor, this manuscript analyses the impact of multipath phenomena on the results of the sensing operation. Multiple bounces in snow layers may degrade the received signal and corrupt the estimation results. Multipath phenomena are well known and analysed in communication systems, but snow layering measurement is a different problem, with little previous research done on related multipath phenomena. This paper analyses the problem with a multi-receiver radar architecture, investigating the impact of multi-path effects from layered snow on measurements carried out by a multi-static radar of Frequency-Modulated Continuous Wave (FMCW) type. A few simplifying assumptions are formulated, in order to make the problem manageable, and a possible approach for solving the related issues is proposed, using a robust algorithm based on pattern matching to detect snow layering in such a challenging environment. Results obtained from various simulations carried out with realistic environmental and geometrical parameters appear to support the effectiveness of the proposed method. Results show that with 3 metres of snow thickness, the probability of successfully detecting the correct numbers of layers exceeds 80%. [ABSTRACT FROM AUTHOR]

Ship detection in Synthetic Aperture Radar (SAR) images has always been a hot topic for research. The development of Deep Neural Networks (DNNs) has strongly promoted the development of computer vision. DNNs are also increasingly applied to SAR ship detection. However, SAR ship detection still faces the following problems: (i) The network used for detection needs to be pre-trained on ImageNet, but there is a large bias between SAR images and ImageNet, which leads to training bias. (ii) The sizes of ship targets vary greatly, and many DNNs do not perform well on multi-scale and small-size SAR ship detection. Therefore, we have designed a SAR ship detector that does not require pre-training. We use DetNet as the backbone network, adopting stacked convolution instead of down-sampling to solve the problem of small object detection and adopt a feature reuse strategy to improve parameter efficiency. In addition, we introduce several branches in the proposal sub-network to provide multi-scale object detection. In the detection sub-network, we use position-sensitive region of interest pooling to improve the prediction accuracy. Experiments on SAR ship dataset prove that our method performs better than some pre-trained networks for small ship detection and complex background ship detection. [ABSTRACT FROM AUTHOR]