Search Results (5,834)

Topographic and geomorphic information provides an essential basis for human development and utilization of natural resources, disaster prevention and mitigation, ecological environment protection, and scientific research. Among spaceborne remote sensing approaches, Synthetic Aperture Radar (SAR) stands out due to its ability to actively transmit and receive microwave signals, enabling high spatial coverage, all–weather, and all–day observation. With the rapid development of miniaturized satellite constellations, high–revisit and high–resolution SAR data have become more accessible, offering unprecedented opportunities for dynamic ocean observation. However, existing SAR–based bathymetry methods based on power–spectrum analysis are susceptible to sea–spike noise and 180° directional ambiguity, limiting their accuracy in shallow coastal waters. To address these limitations, a Cross–Spectrum–based Wave Ray Tracking bathymetry retrieval algorithm (CS–WRT) is developed using high–resolution imagery from mini–SAR constellations including HiSea–1 and Chaohu–1. The method incorporates cross–spectrum analysis into a localized wave ray tracking framework to effectively suppress sea–spike noise and accurately extract shallow–water wave vectors. Applied to six SAR images over the Taiwan Strait, CS–WRT consistently outperformed the power–spectrum approach in coastal environments. In the Jinjiang coastal region, comparison with Electronic Navigational Chart (ENC) data yielded a root mean square error of 2.22 m, a mean absolute percentage error of 7.06%, and a Pearson correlation coefficient of 0.84. Analysis of the shoaling slope parameter k further revealed that stronger wave shoaling effects correlate with improved retrieval accuracy, suggesting its potential as a diagnostic indicator of retrieval reliability. Full article

Generating synthetic aperture radar (SAR) images at unknown azimuth angles under limited sample conditions remains a challenging task, since target scattering characteristics vary significantly with observation angle and are difficult to model effectively using only image-domain priors. To address this issue, this paper proposes a scattering center prior-guided conditional diffusion framework for unknown-azimuth SAR image generation. First, the Iterative Shrinkage–Thresholding Algorithm (ISTA) constrained by the Point Spread Function (PSF) is employed to extract dominant scattering centers from SAR images at known viewing angles, obtaining sparse and physically interpretable scattering center maps. Subsequently, the target category, azimuth angle, and scattering map are jointly used as conditional vector inputs to train the diffusion model. During the inference stage, scattering maps from known azimuth angles are fused to construct a scattering prior for the unknown azimuth, which is used to guide the generation of the corresponding SAR image. Experimental results under sparse angular sampling conditions demonstrate that, compared with the scattering-guided GAN baseline and the non-learning image-domain interpolation baseline, the proposed method better preserves dominant scattering structures and generates unknown-azimuth SAR images with clearer target contours, more stable strong scattering regions, and fewer local artifacts. In summary, introducing dominant scattering center priors into the conditional diffusion model provides effective physical constraints for SAR image generation and improves unseen-azimuth SAR image generation under the evaluated sparse angular sampling conditions. Full article

The peak sidelobe level (PSL) of an orthogonal waveform set directly governs the range and azimuth ambiguities of synthetic aperture radar (SAR) images and is therefore a key figure of merit for the imaging quality of multiple-input multiple-output SAR (MIMO-SAR). Minimizing the PSL is an NP-hard problem with a non-differentiable objective, and existing approaches often suffer from high computational cost and limited scalability while achieving only suboptimal PSL suppression. We therefore propose a loss function built around the 1.5-entmax sparse transform, which is simultaneously differentiable, sparse, and adaptive. Analysis of its differentiability and derivation of its analytical gradient allow us to recast the NP-hard problem as a differentiable optimization problem that can be solved by well-established algorithms. Owing to the sparsity of the transform, the gradient is concentrated on the high-energy sidelobes, while the gradient contribution from low-energy sidelobes becomes exactly zero, removing the gradient noise contributed by low-energy sidelobes and yielding a lower PSL. To overcome the scale sensitivity of the 1.5-entmax function, the upper bound on the sidelobe magnitude is used to normalize the input, which makes the threshold adaptive and removes the need for additional loss-function hyperparameter tuning across waveform-design tasks of different sizes. To further improve computational efficiency, we combine gradient descent with a deep learning framework: the waveform phases are treated as the learnable parameters of a neural network-like model, thereby yielding a back-propagation-based optimization framework with graphics processing unit (GPU) parallelism. The algorithm is implemented for parallel execution on GPU, and the gradient is computed efficiently through the network’s automatic differentiation in conjunction with a custom analytical-gradient operator, leading to a substantial increase in computational speed. Without the need for the manual tuning of loss-function hyperparameters, the proposed algorithm achieves the lowest PSL across waveform sets of various sizes and reduces computation time by approximately two orders of magnitude in large-scale settings. Full article

Multi-sensor fusion of optical and synthetic aperture radar (SAR) imagery is widely used for land cover classification, yet most studies treat heterogeneous sensors as a uniform feature pool, leaving class-dependent differences in sensor contribution insufficiently quantified. To address this gap, this study integrates optical and SAR imagery within a Random Forest (RF) classifier in Google Earth Engine (GEE) and applies a combined SHapley Additive exPlanations (SHAP)—Remove and Retrain (ROAR)—bootstrap framework to disentangle, for each class, which sensor provides which discriminative information and how faithful those attributions are. The dataset included Sentinel-1/2, Korea Multi-Purpose Satellite (KOMPSAT)-3A/5, and Landsat-8, representing a range of spatial resolutions and spectral characteristics. An RF-based machine learning (ML) model was employed to perform multi-sensor data fusion and classification. To address the inherent opacity of ML models, we employed the SHAP algorithm, an explainable artificial intelligence (XAI) method, to interpret classification decisions. SHAP analysis indicated that visible and near-infrared (NIR) bands, along with vegetation indices, were the dominant contributors to land cover classification in this study area, while SAR data provided complementary structural information for spectrally ambiguous targets, such as roads—a class-dependent role reflected in the stable rank ordering of the Sentinel-1 VV contribution across bootstrap replications, rather than in a formally significant magnitude difference. ROAR results were consistent with the top-ranked SHAP features being those on which the classifier relies, supporting a physically plausible interpretation of ML-based remote sensing classification. These attributions were robust to estimator choice (SHAP-permutation ρ = 0.89) and spatial partitioning (mean ρ = 0.97 across folds). The contribution of this study is methodological rather than algorithmic: it provides an integrated analytical framework that applies SHAP, ROAR, and bootstrap confidence intervals jointly to a specific multi-sensor land cover problem, demonstrating that interpretability and high classification accuracy can be reported together. The results demonstrate that optical and SAR fusion contribute differently across land cover classes rather than uniformly, providing practical, class-specific guidance for sensor selection in operational land cover mapping and improving the interpretability of machine learning-based mapping workflows. Full article

In conventional spaceborne Synthetic Aperture Radar (SAR) systems, raw echo data are usually downlinked to ground stations for image formation, resulting in substantial communication burden and processing delay. Although onboard SAR imaging can alleviate this problem, onboard processors are constrained by power consumption, memory capacity, thermal dissipation, and physical size. Therefore, low-power embedded GPU platforms have become a practical choice for onboard SAR processing. Existing onboard processing is mainly suitable for relatively low-complexity imaging modes and algorithms, while high-resolution sliding spotlight SAR requires more accurate frequency-domain processing due to its extended azimuth bandwidth, strong range–azimuth coupling, and nonlinear range cell migration. The $\omega$-k algorithm is well-suited for such high-resolution imaging scenarios, but its large-scale FFTs, phase compensation, and Stolt interpolation impose significant pressure on the memory capacity, memory bandwidth, and computational resources of embedded GPU platforms. To address these challenges, this paper presents a GPU-oriented $\omega$-k imaging framework for high-resolution sliding spotlight SAR on the Jetson AGX Orin embedded platform. The proposed framework formulates a complete sliding spotlight $\omega$-k processing flow and develops GPU-oriented optimization strategies for efficient execution on the embedded GPU platform. Specifically, hybrid data partitioning is designed to adapt memory access patterns to range- and azimuth-dominant stages, an asynchronous multi-stream pipeline with reusable GPU buffers is introduced to overlap data movement and computation, and customized kernels are developed for Stolt interpolation and FFT-related spectral centering. Experiments on simulated sliding spotlight SAR data demonstrate that the proposed method achieves well-focused imaging results with consistent impulse response characteristics. For a 32,768× 32,768 simulated SAR dataset, the proposed implementation achieves an end-to-end imaging time of 32.17 s on Jetson AGX Orin. These results demonstrate the feasibility of the proposed framework for onboard high-resolution SAR imaging on an embedded GPU platform. Full article

Optical vegetation and soil indices are widely used in Earth observation, although their estimation is strongly affected by cloud coverage and illumination variability. Synthetic-aperture radar (SAR) has therefore attracted increasing interest as an alternative source for spectral index prediction. Most existing studies focus on directly estimating a single index from SAR observations. In this work, we investigate a more flexible formulation in which Sentinel-2 multispectral bands are first reconstructed from Sentinel-1 SAR data and subsequently used to derive multiple spectral indices. Experiments are conducted on the SEN12TP dataset, exploiting near-synchronous paired Sentinel-1 and Sentinel-2 acquisitions together with auxiliary elevation and land-cover information. Three SAR-to-multispectral reconstruction strategies are compared, namely, Efficient-UNet, Pix2Pix, and a conditional flow matching model. The resulting indices are then evaluated against those obtained through dedicated index-specific reconstruction models. The results show that Efficient-UNet achieves the best overall multispectral reconstruction performance among the evaluated architectures. Moreover, indices derived from reconstructed multispectral bands achieve performance comparable to dedicated index-specific models while offering substantially greater flexibility, as multiple indices can be computed within a single framework without retraining task-specific models. At the same time, the experiments highlight important intrinsic limitations of SAR-based spectral reconstruction. Although the reconstructed products preserve the large-scale spatial organization of the scenes, they do not fully recover fine spectral and vegetation-sensitive details. Consequently, SAR-derived spectral indices should be regarded as approximate proxies of optical observations rather than direct substitutes, particularly in applications requiring accurate biophysical interpretation. Full article

Near-field beam shaping for phased-array antennas operating in the Fresnel region is a challenging non-convex electromagnetic synthesis problem, requiring coherent control of the radiated fields while accounting for the distinct positions, radiation patterns, and polarization states of individual array elements. This paper presents a physics-informed optimization framework for near-field beam shaping based on a unified vector formulation that enables the direct coherent summation of the electromagnetic fields radiated by array elements despite their distinct local spherical coordinate systems. Unlike conventional formulations that rely on repeated transformations between local spherical and global Cartesian coordinate systems, the proposed representation preserves the physical polarization properties of the electromagnetic field while providing a rigorous framework for near-field beam synthesis. To optimize the electromagnetic energy distribution over finite target surfaces rather than a single focal point, an analytical near-field point-focusing solution is integrated into the optimization process through a physically informed initialization strategy. The resulting non-convex optimization problem is solved using a genetic algorithm (GA) to determine the element phase distribution that maximizes electromagnetic energy within the prescribed target region while minimizing undesired field leakage. The proposed methodology is validated through full-wave electromagnetic simulations and extensive experimental measurements using a dedicated phased-array platform, including the design, fabrication, characterization, and calibration of the antenna array and phase-control network. The results demonstrate flexible near-field beam shaping and controlled energy focusing over finite target regions. The proposed framework is applicable to biomedical radar sensing, near-field synthetic aperture radar (SAR) illumination, wireless power transfer (WPT), high-power microwave (HPM) systems, and near-field millimeter-wave communications. Full article

Accurate and transferable land cover mapping from satellite imagery is a prerequisite for national-scale agrienvironmental monitoring and climate change impact assessment. The joint use of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery offers complementary structural and spectral information, yet systematic evaluation of fusion strategies and their geographic transferability remains limited. We trained and tested five U-Net fusion architectures—S1-only, S2-only, early (input-level), feature-level (middle), and decision-level (late)—alongside a SegFormer-b2 transformer baseline over two German cities (Munich and Berlin) using the Multi-Sensor Land Cover Classification (MSLCC) dataset (single-date 2017 Sentinel-1B/Sentinel-2A acquisitions) at 10 m resolution. Three cross-city transfer protocols (Munich → Berlin, Berlin → Munich, and combined training) quantify model transferability across contrasting urban–rural gradients. Early fusion achieved the highest in-city macro-averaged F1 score among U-Net variants (0.8278), a small but statistically significant improvement over the optical-only baseline (0.8236; patch-level paired bootstrap, $p=0.006$); feature-level (middle, 0.8164) and decision-level (late, 0.8196) fusion were, by contrast, significantly worse than the optical-only baseline ($p<0.001$ and $p=0.030$, respectively), and the SAR-only model (0.6864) trailed substantially. The built-up class was the primary beneficiary of SAR inclusion under early fusion. SegFormer-b2 (0.8214) was numerically close to, but statistically significantly below, the best convolutional configuration ($p=0.005$), and exhibited strong cross-city transfer (0.8632–0.8608 macro-F1), consistent with the geographic invariance conferred by its ImageNet-pretrained encoder. Combined training across both cities improved over the single-direction transfer average by 0.012 macro-F1 points for U-Net and 0.006 points for SegFormer, offering a practical route to national-scale deployment without requiring explicit domain adaptation. Spectral index augmentation (NDVI, NDWI, ExG) and SE channel attention did not significantly improve over plain early fusion when derived from percentile-normalized inputs, with the best variant statistically indistinguishable from the baseline at macro-F1 = 0.8268 ($p=0.365$); the result is attributable to a specific preprocessing dependency: NDVI, NDWI, and ExG are only physically meaningful when computed from calibrated reflectance, whereas here they were derived after scene-level 2nd–98th percentile stretching, which strips the absolute radiometric referencing the indices rely on; practitioners combining spectral indices with percentile-normalized (rather than physically calibrated, e.g., Level-2A surface-reflectance) inputs should expect a similar null result. Full article

The surface subsidence caused by coal mining is a geological environmental disaster that restricts the sustainable development of mining areas. Traditional monitoring methods have limitations in long-term and high-precision observation. Therefore, this paper proposes a synergistic monitoring framework for mining subsidence under thick loose layers by integrating Interferometric Synthetic Aperture Radar (InSAR) technology and Unmanned Aerial Vehicle (UAV) photogrammetry. The research results show: (1) UAV photogrammetry can accurately obtain the large gradient deformation at the center of the subsidence basin, while InSAR has better accuracy at the basin edge. The proposed fusion method is significantly superior to a single method. (2) The parameters obtained by the probability integral method based on the fused data are in good agreement with the parameters obtained by leveling measurement data, and the relative error of the parameters is less than 4%. (3) The thick and loose-layered mining areas have the characteristics of larger subsidence, steeper gradient at the center, slow convergence at the edge, and wide influence range. This study provides a new approach for precise subsidence monitoring, and the revealed subsidence characteristics provide a scientific basis for disaster assessment and mining optimization in similar areas. Full article

Traditional motion compensation (MoCo) methods for high-resolution synthetic aperture radar (SAR) rely on iterative processing between imaging and MoCo, incurring heavy computational overhead and barely meeting fast imaging requirements. To address this, this paper presents a non-iterative SAR autofocus method based on multi-subimage two-dimensional phase gradient autofocus (2D-PGA). Leveraging the parallel imaging capability of the ground Cartesian back-projection (GCBP) algorithm, we reveal an a priori 2D spatially variant spectral structure of GCBP subimage errors, then the proposed 2D-PGA method is utilized to precisely estimate subimage wavenumber-domain errors. Mapping between subimage offsets and linear phase errors is derived for cross-subimage error splicing. The spliced errors are compensated for corresponding subimages to realize autofocus and ensure coherent subimage fusion, enabling non-iterative parallel processing of SAR time-domain imaging and MoCo. Experiments with 0.03 m-resolution Ku-band microwave photonic SAR measured data verify the necessity and effectiveness of the proposed method. Full article

Regional landslide susceptibility mapping commonly relies on static conditioning factors, including terrain, geology, hydrology, land cover and human activity. These factors describe long-term instability settings but cannot directly represent recent or ongoing ground deformation. Interferometric Synthetic Aperture Radar (InSAR) can provide spatially distributed deformation information, yet mountainous InSAR evidence is affected by uneven observation availability, vegetation decorrelation, terrain-induced geometric distortion and confounding between observation support and static environmental conditions. These issues make it difficult to determine whether radar-derived variables represent deformation signals or mainly indicate where observations are reliable. This study develops a scale-sensitive and confounding-audited Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) evidence representation framework for regional landslide susceptibility mapping. In Pingwu County, China, a 1607-record landslide inventory was converted into 1579 unique 30 m landslide cells, with 1393 for training and 186 for inventory-concentration validation. Fourteen static factors formed the baseline model. Deformation evidence from 98 Sentinel-1A descending acquisitions was represented using point-based line-of-sight (LOS) variables, neighbourhood component descriptors, a compressed deformation-intensity and observation-availability index, and a separated deformation intensity (DI) plus observation availability (RI) representation. Results show that the static factors already provided a strong first-order baseline. Adding SBAS-InSAR evidence did not produce uniform paired improvements across models, metrics or neighbourhood scales. In 10 km spatial-block cross-validation, random forest models using static factors, 300 m component descriptors and 300 m DI + RI features achieved similar mean area under the receiver operating characteristic curve values of 0.948, 0.949 and 0.950, with mean Matthews correlation coefficient values of 0.791, 0.795 and 0.795. Broader neighbourhood representations may expand the attainable performance boundary, but only as diagnostic evidence. SBAS-InSAR-derived information should therefore not be treated as a simple additional conditioning factor; its value depends on jointly interpreting deformation intensity, observation availability and their confounding with static context. Full article

Freeman−Durden Decomposition (FDD) and Non-Negative Eigenvalue Decomposition (NNED) are among the most widely used incoherent polarimetric decomposition algorithms for analyzing fully Polarimetric Synthetic Aperture Radar (PolSAR) data, particularly FDD. However, with advancements in model-based incoherent polarimetric decomposition techniques, their original algorithms have certain aspects that can be modified to enhance their decomposition performance. These aspects include: FDD occasionally yielding negative power values and typically overestimating the power of the volume scattering component; the scattering mechanism of the remainder matrix in NNED being further interpretable; and the potential for improving its decomposition performance through specific modifications. Therefore, two improved incoherent polarimetric decomposition algorithms, Modified Freeman−Durden Decomposition (MFDD) and Deorientation Non-Negative Eigenvalue Decomposition (DNNED), are proposed in this study. For MFDD, deorientation is applied at the outset, and two additional steps are introduced to eliminate negative power values in the decomposition results. The DNNED algorithm also employs deorientation and enhances the interpretation of the scattering mechanism of the remainder matrix. DNNED identifies that the remainder matrix corresponds to a dihedral with a 45-degree orientation angle, thus classifying its power as double-bounce scattering. Decomposition performance tests have been conducted using two actual PolSAR images derived from E-SAR of Germany and GF-3 of China. Experimental results demonstrate that the performance of MFDD is superior to that of FDD, and the performance of DNNED is the best among the four aforementioned decomposition algorithms. Full article

Continuous monitoring of vineyard dynamics is essential for optimizing viticultural practices and assessing plant health. While the seasonal behaviors of satellite-derived vegetation indices are widely studied, robust parametric modeling of these temporal trends remains underexplored. Building upon initial clues derived from Italian vineyards, this study proposes a novel analytical framework based on the consistent parabolic temporal signature of optical and Synthetic Aperture Radar (SAR) indices. Focusing on the elevated Trinity Canyon Vineyards in Armenia, we model the yearly evolution and temporal aggregations of these indices using a parabolic fitting approach. Our results suggest that the parabola vertex, which we hypothesize corresponds to the absolute maximum of vegetative activity, remains remarkably stable across diverse vine types, satellite orbits, and years. While this stable behavior suggests an underlying phenological or structural consistency, distinct exceptions to this trend have also been identified and considered. Furthermore, to bridge the gap between remote sensing observables and agronomic traits, we investigated the relationship between the fitted parabolic parameters and the Winkler index, which is used here as an estimator of above-ground biomass (AGB). By correlating the vegetation indices’ temporal dynamics with biomass growth and by isolating specific anomalies driven by environmental or anthropogenic factors, this work offers a basis for a predictive methodology that enables tracking vineyard structural development. Full article

Atmospheric artifacts can obscure tectonic deformation in interferometric synthetic aperture radar (InSAR) observations, while conventional correction methods often depend on external atmospheric data. This study proposes a supervised residual-learning framework that directly predicts noise components in unwrapped InSAR maps rather than reconstructing deformation signals. Physically informed synthetic datasets were generated by combining Okada and Mogi deformation models with topography-correlated tropospheric delays, spatially correlated turbulent noise, and long-wavelength ramps. A network-depth sensitivity analysis identified a 20-layer denoising convolutional neural network as the optimal balance between accuracy and model complexity. Tests on independent synthetic datasets showed that the model reliably distinguished deformation from noise when the signal-to-noise ratio exceeded approximately 10⁻¹, whereas performance degraded under extremely noise-dominated conditions. The framework was further evaluated using ALOS/PALSAR and ALOS-2/PALSAR-2 observations of post-eruptive deformation at Mt. Ontake, Japan, and coseismic deformation associated with the 2009 L’Aquila earthquake, Italy. Compared with uncorrected, GACOS-corrected, and linear-corrected results, the CNN correction reduced topography-correlated and long-wavelength artifacts, improved temporal consistency, and generally achieved closer agreement with GNSS observations. These results suggest that residual noise learning provides an efficient approach for automatic atmospheric correction of unwrapped InSAR observations, with the potential for transferability across different deformation-source settings. Full article

In millimeter-wave frequency-modulated continuous-wave (FMCW) multiple-input multiple-output synthetic-aperture radar (MIMO-SAR) imaging, platform displacement beyond the spatial Nyquist limit during a slow-time sampling interval creates aperture gaps, causing azimuth aliasing and degraded resolution. This paper proposes a spectral-prior-guided Swin TransUnet (SSTU) method for suppressing azimuth ambiguity in sparse moving-array imaging. Gaussian soft labels derived from point-scatterer positions formulate localization as heatmap regression and guide mainlobe learning. A two-dimensional fast Fourier transform (2D-FFT) layer then constructs a range–azimuth spectrum that exposes main peaks, sidelobes, and periodic grating lobes. A convolutional encoder extracts local spectral features, Swin Transformer blocks model long-range ambiguity correlations, and a U-Net-style multiscale decoder reconstructs high-resolution range–azimuth images. Simulations show that SSTU reliably recovers multiple point targets from noise and grating lobes despite substantial aperture gaps. At 60% aperture sparsity and signal-to-noise ratio (SNR) above −6 dB, it achieves a root mean square error (RMSE) below ${10}^{-2}$ and an azimuth ambiguity suppression ratio better than −30 dB, outperforming conventional methods. Measurements using a 77 GHz radar platform further demonstrate high-quality outdoor imaging of randomly distributed strong scatterers at 60% moving-aperture sparsity. Full article