Search Results (1,360)

Spaceborne microwave imagers are vital for monitoring global precipitation due to their wide swath and high sensitivity. This study proposes a deep learning approach that integrates a U-Net with a multi-task learning (MTL) framework. The model was separately trained over land and ocean using GPM Microwave Imager (GMI) brightness temperatures, with collocated precipitation rates and types from the Dual-frequency Precipitation Radar (DPR) as labels. This combines the accuracy of radars with the coverage of imagers to produce high-precision, wide-swath precipitation estimates. In the MTL setup, near-surface precipitation rate retrieval is the main task, and precipitation type classification is the auxiliary task. A composite loss (weighted MSE and quantile regression) is used for the main task, and weighted cross-entropy for the auxiliary task. Residual blocks and an attention mechanism are incorporated to improve physical representation and generalization, thereby significantly enhancing the model’s capability to retrieve heavy precipitation. The model was trained on 2015–2024 GPM data and evaluated on an independent six-month 2025 GMI dataset. Compared to a standard U-Net, the MTL model achieved significant gains: Pearson Correlation Coefficient (PCC) increased by 9.7% (ocean) and 13.7% (land), and Critical Success Index (CSI) by 10.7% (ocean) and 10.8% (land). The method was also applied to the FY-3G Microwave Radiation Imager (MWRI-RM). In case studies, it outperformed the official product, achieving average increases of 20.1% in PCC and 15.7% in CSI, respectively. Validation against FY-3G Precipitation Measurement Radar (June–August 2024) yielded over ocean PCC = 0.757, RMSE = 1.588 mm h⁻¹, MAE = 0.355 mm h⁻¹; over land PCC = 0.691, RMSE = 2.007 mm h⁻¹, MAE = 0.692 mm h⁻¹. The study demonstrates that the MTL-enhanced U-Net significantly improves the accuracy of spaceborne microwave imager rainfall retrieval and shows robust practical applicability. Full article

Wet path delay (WPD), required to correct sea-level measurements from satellite altimetry, is routinely estimated using observations from onboard microwave radiometers (MWR). However, when MWR retrievals are invalid or absent, WPD is generally obtained from atmospheric models, unless observations from external sources, such as scanning imaging radiometers, are available in spatial and temporal proximity to the altimeter measurements. These external observations, however, provide total column water vapor (TCWV) rather than WPD, and a reliable TCWV-to-WPD conversion is necessary. Current state-of-the-art conversions use TCWV only or TCWV and near-surface air temperature. The first approach is particularly relevant when external sources provide TCWV only. In this context, this paper presents, first, a comprehensive intercomparison of the methods available in the literature and, second, an improved TCWV-to-WPD conversion. The results show that one of the existing functions underestimates WPD by up to 1.6 cm in regions of high water vapor content, while another provides accurate WPD values only under specific atmospheric conditions. This study proposes an updated methodology that yields accurate WPD across the entire TCWV range, highlighting the importance of a reliable TCWV-to-WPD conversion for accurate sea-level estimation when valid MWR observations are unavailable. Full article

In ultra-wideband (UWB) synthetic aperture radar (SAR) imaging, in-band antenna standing waves (SW) can generate range ghosts, degrading image quality. To address this issue, an image-domain suppression method is proposed, leveraging the phase symmetry property (PSP) between the SW signal and its mirror SW (MSW) signal. Based on PSP, the MSW signal is rapidly constructed from the SW signal, ensuring that both share the same target region but exhibit different ghost regions. PSP is further extended to the image domain. Specifically, the SW-induced phase is extracted in the wavenumber domain. Based on the PSP, this phase is then used to construct the MSW signal, which exhibits a phase spectrum that is symmetric to that of the SW signal with respect to the origin. The MSW image is subsequently fused with the original SAR image, thereby effectively suppressing SW-induced ghosts. The experimental results demonstrate that the proposed method significantly mitigates ghosting while preserving the amplitude and structural integrity of the main signal, thereby enhancing overall imaging quality. Full article

Ka-band carrier-frequency-agility (CFA) synthetic aperture radar (SAR) employs pulse-to-pulse random wide-range frequency hopping to enhance anti-interference capability. However, the random hopping disrupts the azimuth phase continuity, and the millimeter-wave wavelength of the Ka band makes the imaging quality extremely sensitive to motion errors. To address these challenges, this paper proposes an auto-focusing imaging framework and performs a performance analysis for Ka-band CFA SAR. First, a back-projection (BP)-based imaging model is derived to restore the coherent phase history from the hopped echoes. Second, to compensate for the residual phase errors inevitable in high-resolution millimeter-wave imaging, an auto-focusing framework is developed. This framework incorporates a dynamic sub-aperture strategy and an adaptive spectral notching mechanism to ensure precise phase error estimation in complex scattering environments. Furthermore, the imaging performance under different frequency-selection modes is analyzed to provide a guideline for the parameter selection of the Ka-band CFA SAR. Experiments with a vehicle-mounted Ka-band SAR system demonstrate that the proposed method achieves well-focused images with 5 cm resolution. Full article

Many patients with primary or metastatic lung cancer are not candidates for surgery, additional radiation, or further systemic therapy due to advanced age or comorbidities; this creates a need for minimally invasive locoregional options. Image-guided thermal ablation (IGTA) is being applied across a broader spectrum of lesions, while bronchial artery chemoembolization (BACE) is emerging as a therapy option for treatment-refractory advanced disease. Recent studies in thermal ablation have focused on optimizing energy delivery and protocols, as well as improving ablation zone predictability and analysis. Advances in lesion targeting, including cone beam CT fusion, electromagnetic guidance, and robotic-assisted ablation, allow for treatment of subcentimeter and ground-glass lesions in anatomically challenging locations. Growing clinical experience supports IGTA for intrathoracic oligoprogression and as salvage therapy after recurrence. In the endovascular space, improved imaging, microcatheters, and drug-eluting microspheres have expanded the use of BACE for disease and symptom control in advanced lung cancer. Multimodal strategies combining minimally invasive locoregional treatments with systemic therapies and radiation are being explored, with early data showing improvements in survival without increased toxicity. This narrative review synthesizes emerging techniques, clinical data, and indications for percutaneous and endovascular lung cancer treatments and underscores the need for prospective and randomized trials to refine patient selection, treatment sequencing, and long-term outcomes. Full article

Microwave imaging is emerging as an alternative to conventional medical diagnostic techniques. Traditional analytical and numerical methods fail to adequately address these fundamental challenges: they often rely on strict linear approximations or simplified physical models, leading to low reconstruction accuracy, poor robustness, and limited generalization ability in complex clinical scenarios. As a result, they cannot meet the high-precision requirements of practical stroke microwave imaging. To further improve the accuracy of microwave imaging algorithms in recognizing stroke regions and solving the backscattering problem, this study employs a combination of methods with deep learning. It presents the Multi-Scale Attention Network (MSA-Net) for microwave imaging. The network is based on the EGE-UNet network structure with improved multi-axis Hadamard attention, incorporating null-space pyramid pooling and introducing a deep supervisory mechanism to improve the network performance further. To combine microwave imaging with deep learning, firstly, a large amount of microwave data need to be simulated with HFSS, in which the simulation model is a human brain stroke model constructed by an HFSS simulation system. Secondly, the microwave data obtained from the simulation are converted into a tensor format. Then, the tensor data are input into the MSA-Net neural network, which generates a binary mask image that can be used to detect the size and location of the stroke. This study also prompts the model to converge faster by sparsifying the microwave data to improve training efficiency. The method has been tested using simulation data, and based on the comparison experiments with other networks, MSA-Net is more accurate in detecting the location and the bleed size. The experimental results show that the proposed method is superior for stroke imaging. The experimental results show that the proposed model achieves a 1.08 improvement in peak signal-to-noise ratio and a 0.017 reduction in learned perceptual image block similarity, fully validating the effectiveness of the structural optimization strategy proposed in this paper. Full article

Model-based polarimetric SAR (PolSAR) algorithms for bio- and geophysical parameter estimation rely on the effective separation of the combined scattering response of vegetation canopies and the soil surface through physically based models. However, the interpretation of polarimetric features derived from physical models is still subject to some ambiguity. Another strategy for complementing the model-based approaches for scattering mechanisms characterisation deals with the separation of the polarised and depolarised contributions of the PolSAR data according to their degree of polarisation. In this paper, we propose a two-component decomposition for estimating the depolarised and polarised components within the target and their corresponding coherency matrices. The method requires the previous calculation of the backscattering powers given by the model-free three-component (MF3C) decomposition, which in turn relies on the 3-D Barakat degree of polarisation. This quantitative information allows us to construct an inversion algorithm to retrieve the proportion of the polarised and depolarised contributions for all the elements of the observed coherency matrix under the reflection symmetry assumption. In essence, the proposed decomposition can be regarded as an extension of the MF3C method and, as a consequence, it enables the exploitation of both model-free and model-based approaches by using a physical rationale driven by the capability of the 3-D Barakat degree of polarisation. Therefore, practical applications can benefit from this approach as the retrieval of target parameters could presumably be done in a more accurate way by directly applying existing scattering models to both components. Indoor multi-frequency datasets acquired over three vegetation samples from the European Microwave Signature Laboratory (EMSL) and P-, L-, and C-band AIRSAR images over a boreal forest in Germany have been employed for testing the proposed decomposition. Performance analysis was performed using different polarimetric tools applied to the outcomes of the two-component decomposition, namely, the eigendecomposition and the copolar cross-correlation analysis of polarised and depolarised components, as well as histograms and a correlation analysis among backscattering powers. Overall, it has been observed that the method outputs are consistent with the theoretical expectations for the depolarised and polarised scattering components for a wide range of scenarios and sensor frequencies. Full article

This work presents the design of a compact, wideband circular microstrip patch antenna for microwave imaging-based brain tumor detection. The main contribution is the development of a compact antenna structure incorporating enhanced ground-plane slot modifications, which significantly improves impedance bandwidth while maintaining a small electrical size, making it highly suitable for medical imaging systems. In addition, the study integrates antenna design, safety evaluation, and microwave imaging analysis within a unified framework to assess tumor localization feasibility using a realistic head model in CST Microwave Studio. The proposed antenna is fabricated on an FR-4 substrate with dimensions of 37 × 54.5 × 1.6 mm³, corresponding to an electrical size of 0.176λ × 0.260λ × 0.0076λ at the lowest operating frequency of 1.43 GHz. Ground-plane slot enhancements are introduced to achieve wideband performance, resulting in an impedance bandwidth from 1.43 to 4 GHz and a fractional bandwidth of 94.7%. The antenna exhibits a maximum realized gain of 3.7 dB. To evaluate its suitability for medical applications, specific absorption rate (SAR) analysis is performed using a realistic human head model at multiple antenna positions and at 1.5, 2.1, 2.5, 3.3, and 3.9 GHz frequencies. The computed SAR values range from 0.109 to 1.56 W/kg averaged over 10 g of tissue, satisfying the IEEE C95.1 safety guideline limit of 2 W/kg. For tumor detection assessment, time-domain simulations are conducted in CST Microwave Studio using a monostatic radar configuration, where the antenna operates as both transmitter and receiver at twelve angular positions around the head with 30° increments. The collected scattered signals are processed using the Delay-and-Sum (DAS) beamforming algorithm to reconstruct dielectric contrast maps and localize the tumor. It should be noted that the tumor-imaging demonstrations presented in this work are based on numerical simulations, while experimental validation is limited to the characterization of the fabricated antenna. Nevertheless, the findings indicate that the proposed antenna is a promising candidate for noninvasive, low-cost microwave brain tumor imaging applications. Full article

Numerous studies have demonstrated the potential of Synthetic Aperture Radar (SAR) in monitoring glacier velocity. However, owing to the complex dynamics of glaciers and the variability of their surface features, velocity fields derived from even short-interval SAR image pairs often exhibit missing parts. This study proposes a missing glacier velocity recovery method based on a spatiotemporal hybrid neural network to solve the above problem. Considering the spatiotemporal characteristics of glacier velocity fields, a hybrid network combining an Artificial Neural Network (ANN) and a Denoising Autoencoder (DAE) is developed. The ANN is first employed to capture spatial correlations associated with missing values, after which it is integrated with the DAE to model temporal variations using a time-aware loss function. An iterative weighting strategy adaptively balances spatial and temporal features during training. The method is applied to SAR–derived velocity fields of Petermann Glacier. Experimental results show that the method significantly improves the performance of glacier velocity recovery compared to traditional methods. Additionally, the study compares and analyzes the velocity of Petermann Glacier in different seasons, and the findings indicate that the glacier exhibits more pronounced seasonal differences in the accumulation zone. Full article

High-resolution synthetic aperture radar (SAR) imagery is essential for large-scale road extraction, yet it presents significant challenges due to inherent speckle noise, complex scattering effects, and the anisotropic nature of road structures. Moreover, the scarcity of large-scale, high-quality annotated SAR road datasets hinders the development of deep learning-based methods. To address these issues, this paper first constructs a high-resolution SAR road dataset covering representative regions in the western United States. Road annotations are automatically generated using OpenStreetMap (OSM) vectors and then refined via a structure-guided alignment strategy. Building upon this dataset, we propose a novel framework termed Multi-Scale and Deformable-Attention LinkNet (MSDA-LinkNet), specifically designed to capture thin, direction-sensitive, and geometrically complex road features. The architecture integrates a parallel direction-aware multi-scale convolution module to explicitly model road anisotropy and scale variations, complemented by a deformable attention mechanism to adaptively aggregate contextual information along curved and irregular trajectories. Extensive experiments demonstrate that MSDA-LinkNet consistently outperforms representative approaches across key metrics, including Precision, F1-score, and Intersection over Union (IoU). The released dataset and benchmark provide a solid foundation for future research in high-resolution SAR-based road mapping. Full article

In microwave imaging with finite antenna arrays, the limited number of array elements constrains spatial sampling and degrades reconstruction quality. To enlarge the aperture effectively, virtual antennas are usually adopted. However, it may lead virtual data to dominate the reconstruction process, thereby amplifying artifacts. This work proposes an interpolation-weighted truncated singular value decomposition (IW-TSVD) framework that expands multistatic scattering matrix by using an integer interpolation factor. The proposed method preserves all physically measured antenna data and applies explicit weighting to virtual channels to suppress their influence. Simulations and hardware experiments show that IW-TSVD improves structural similarity index (SSIM), reduces the mean squared error (MSE), and suppresses artifacts compared with conventional TSVD and zero-padding-based interpolated TSVD, without increasing hardware complexity. Full article

Three-dimensional (3D) glacier velocities capture the full dynamic behavior of ice masses. For marine-terminating glaciers, acquiring 3D velocity fields is particularly critical for quantifying ice discharge into the ocean, assessing the stability of floating ice tongues, and constraining ice–ocean interactions that govern submarine melting, calving processes, and freshwater fluxes to the ocean. To further investigate glacier dynamics and elucidate ice–ocean interaction mechanisms, this study analyzed the 3D velocity of the Petermann Glacier throughout 2021 using long-term Sentinel-1 synthetic aperture radar (SAR) observations. First, two-dimensional velocity time series were derived from ascending and descending SAR images, and the glacier’s 3D velocity components were reconstructed based on the geometric relationships between the two viewing geometries. The estimated 3D velocities were then used as prior constraints, and glacier motion was treated as a continuously evolving state variable within a Kalman filtering framework. Multi-track, asynchronous remote sensing observations were integrated into a unified system to obtain a stable and temporally continuous 3D velocity field. Finally, statistical analyses of the 3D velocity time series were conducted to characterize spatiotemporal variations, seasonal patterns, and topographic influences on glacier motion, thereby providing quantitative insights into the dynamic coupling between glacier and ocean. Full article

Monitoring the biophysical parameters of agricultural surfaces is a key issue for food security in the context of climate change. Since 2016, agricultural surfaces can be monitored from space at high spatial resolution (~10/30 m) in the microwave and optical domains owing to radiometer and SAR sensors onboard Sentinel-1, -2 and Landsat-8 satellites. This paper draws on multi-temporal acquisitions over a six-year period to analyze satellite time series for the main winter and summer crops (corn, sunflower, soybean, sorghum, rapeseed, wheat) grown in southwestern France and more widely cultivated around the world. From January 2016 to December 2021, satellite signals extracted at the field spatial scale offer a unique opportunity to monitor agricultural surfaces with a high temporal resolution (every 1 or 2 days) never achieved before thanks to the combination of multi-sensor and multi-orbit data. Analyses on the impact of the topography and satellites’ viewing angles showed that the NDVI values derived from Sentinel-2 and Landsat-8 are very close (r > 0.92) and can be merged to construct multi-annual time series. Angular sensitivity is much more pronounced for radar images; while it demonstrates a weaker cross-polarization and polarization ratio, it is greater for co-polarization. Optical and radar time series are modulated in time and amplitude depending on yearly climatic conditions and agricultural practices. The combined use of the ascending and descending orbits of the two Sentinel-1 satellites makes it possible to detect specific periods (harvest, flowering) for certain crops (wheat and sunflower). The long-term approach has enabled the modeling of satellite time series using double logistic functions with good performance (r > 0.92 on average), allowing the identification of interannual variations of crop development driven by climatic conditions and agricultural practices. Full article

With the increasing demand for three-dimensional positioning in Synthetic Aperture Radar (SAR) systems, multi-view SAR technology is rapidly evolving. Airborne-distributed SAR systems, benefiting from multi-platform collaborative observation, flexible baseline configuration, and synchronous imaging, have become an ideal solution for realizing this technology. However, the flight paths of these platforms are not optimal, and the airborne navigation equipment also suffers from measurement errors, which severely deteriorates the multi-view SAR target positioning accuracy of the airborne-distributed platforms. Currently, research on this issue remains scarce. This paper is based on the multi-view normalized Range Doppler positioning model, introducing platform position errors to derive the Cramér-Rao Lower Bound (CRLB). A detailed positioning accuracy analysis is conducted for different flight paths and various sources of errors, demonstrating that platform position errors are a primary factor affecting target positioning accuracy. To address this, a target positioning method based on inter-platform ranging information is proposed, which imposes constraints on the position of the airborne-distributed platform using inter-platform ranging data, thereby reducing the dependence of target positioning accuracy on platform position errors and enhancing the robustness of three-dimensional positioning for multi-view SAR targets. The effectiveness of the proposed method is verified using measured data, which reduces the 3D positioning error of the target by nearly 60%. Full article

Spaceborne synthetic aperture radar (SAR) absolute radiometric calibration relies on point targets with a known radar cross-section (RCS), such as triangular trihedral corner reflectors (TTCRs). Traditionally, radiometric calibration using TTCRs requires precise alignment of the corner reflector (CR) boresight to the radar line-of-sight (LOS), leading to frequent field operations and high labor dependency. In this study, a novel compact bidirectional trapezoidal CR is proposed to eliminate such alignment reorientations. The novel CR adopts three design considerations: a scalene shape to optimize the boresight elevation angle and enhance the peak RCS; a bidirectional configuration with azimuth fine-tuning to align with the radar LOS for both ascending and descending passes; and trapezoidal plate trimming to reduce the volume and weight without sacrificing RCS performance. An in-orbit validation is conducted in Xi’an, China, using the SuperView Neo 2-03 satellite. The results demonstrate that the imaging quality of the bidirectional trapezoidal CRs is comparable to that of conventional TTCRs, with all the parameters meeting system specifications. The radiometric calibration constant of the bidirectional trapezoidal CR differs from that of the conventional TTCR by no more than 0.27 dB, with a total uncertainty of ~0.33 dB (1σ)—demonstrating that it achieves equivalent radiometric calibration accuracy to TTCRs. The experiment confirms the feasibility and engineering applicability of the bidirectional trapezoidal CR for X-band SAR radiometric calibration. Full article