Search Results (604)

The Hunshandake Sandy Land is located in the transition zone between typical steppe and desertified grassland in Inner Mongolia, where grassland and sandy land are the predominant land-cover types, accompanied by scattered buildings, roads, and lakes. Its ecosystem is fragile and exhibits significant spatiotemporal dynamics. Given the need for reliable land-cover monitoring in such a fragile and dynamically changing ecosystem, spaceborne polarimetric synthetic aperture radar (PolSAR) is adopted for typical land-cover classification to support grassland ecological monitoring and desertification control. However, high-dimensional PolSAR features derived from multiple decomposition strategies usually contain redundancy and correlation, which may weaken the representation of critical discriminative scattering information. To address this issue, we propose a DeepLassoNet-based feature selection method for typical land-cover classification in the Hunshandake Sandy Land. First, a set of conventional polarimetric decomposition features derived from PolSARpro software is extracted to construct an initial high-dimensional feature pool. Next, a dual-module DeepLassoNet is designed by integrating a nonlinear spatial representation module and an explicit feature–response module, so as to learn discriminative spatial representations while preserving direct channel-level relationships between polarimetric features and classification responses. Finally, by introducing hierarchical sparsity constraints and combining gradient updating with proximal mapping, redundant and irrelevant features are progressively eliminated along a sparse feature screening path. Experimental results demonstrate that the proposed method can effectively select discriminative PolSAR features, reduce feature redundancy interference, and improve the classification accuracy and robustness in complex sandy land–grassland transition scenarios. Full article

This paper describes a polarimetric, frequency-modulated continuous wave (FMCW) synthetic aperture radar (SAR) with an ultrawide bandwidth that spans 2–18 GHz. It is being developed as an airborne sensor called SKuSAR. The intent is to generate a set of sub-band images, thereby creating a multispectral SAR. This opens the prospect for new remote sensing algorithms that exploit variations in the scene’s polarization/frequency response occurring over the system’s three octaves of bandwidth. We describe the SKuSAR hardware and the processing steps applied to the FMCW data to create a multispectral SAR. The approach is practically demonstrated using data collected against calibration targets deployed in a field with the system mounted on a truck. This ground-based arrangement provided an inexpensive solution to test and fine-tune the system hardware and processing algorithms. A few complicating factors specific to the ground-based geometry were encountered, such as multipath signal contamination due to ground reflection and rather short data collections that affected attainable azimuth resolutions. Both these factors were identified and analyzed. The measured characteristics follow theoretical predictions rather well, giving confidence that the system meets its expected nominal performance once airborne, with the mentioned limiting factors absent or of reduced significance. Full article

Polarimetric synthetic aperture radar (PolSAR) calibration quality assessment is essential for verifying the reliability of polarimetric calibration results and ensuring the accuracy of subsequent quantitative applications. The corner-reflector-based assessment is accurate but depends on field deployment and maintenance, whereas the distributed-target-based methods is easier to automate but is sensitive to mixed scattering within image patches and to non-unique histogram peaks. In addition, the parameter distribution may contain multiple peaks, affecting the uniqueness and stability of assessment. To address these problems, this paper proposes a robust distributed-target-based PolSAR calibration quality assessment (RD-PCQA) method without corner reflectors (CRs) to address these problems. The proposed method first uses hypothesis testing of confidence interval method for PolSAR calibration (PCHTCI) to extract high-quality distributed targets and combines the polarimetric correlation coefficient R_HHVV to select volume-scattering-dominant targets, thereby improving the physical consistency of samples used for channel imbalance amplitude (CIA) estimation. Second, a high-proportion distributed-target constraint is used to refine the samples for channel imbalance phase (CIP) and polarimetric crosstalk estimation, reducing the influence of nonideal scatterers on parameter estimation. Finally, a unique peak searching strategy based on progressively enlarged statistical scales is proposed to suppress the effect of multi-peak distributions on assessment result. Experiments were conducted using three GF-3 PolSAR images acquired over the SAR calibration site in Etuoke Banner, Ordos, Inner Mongolia, China, with CR results used as references, and considering finite sample uncertainty. The experimental results show that, compared with the conventional distributed-target-based method, the proposed method is closer to the corresponding CR mean in all comparisons, with the mean absolute deviations for CIA, CIP and crosstalk scenarios reduced to 0.024 dB, 2.043° and 3.069 dB, respectively. Therefore, it demonstrates the effectiveness and practical potential of the proposed method for PolSAR calibration quality assessment without CRs. Full article

Synthetic aperture radar (SAR) object detection remains challenging, as target evidence is often sparse, discontinuous, and heavily influenced by speckle noise, sidelobes, shadows, and clutter-like background scattering. Existing dense detectors usually process SAR images as ordinary grayscale images, which causes feature modulation to rely on unrestricted saliency responses that may simultaneously enhance true targets and bright background scatterers. To address this issue, we propose a SAR-inspired Scattering-Center Field (SCF) modulation framework for multi-scale dense object detection. The SCF module is designed as a tensor-preserving feature adapter that decomposes feature modulation into three internal latent fields: a center-like evidence field, a response-amplitude field, and an orientation-anisotropy field. These fields are inferred from intermediate features through lightweight local, strip-convolution, and contextual branches, and then they are subsequently fused into an identity-initialized residual spatial gate. The proposed module requires no scattering-center annotations, segmentation masks, auxiliary field supervision, phase history, polarimetric data, or additional post-processing. Consequently, it can be seamlessly integrated into standard dense detection pipelines without altering labels or prediction heads. Experiments are conducted on four publicly available SAR detection datasets, namely SSDD, SAR-AIRcraft-1.0, SAR-Ship, and MSAR-1.0. The proposed detector achieves mAP50/mAP50-95 scores of 0.954/0.682 on SSDD, 0.930/0.661 on SAR-AIRcraft-1.0, 0.971/0.676 on SAR-Ship, and 0.688/0.482 on MSAR-1.0. These results indicate that SCF provides a lightweight and detector-compatible modulation mechanism for improving SAR object detection under sparse target responses and cluttered imaging conditions. Full article

In recent years, U-Net-based architectures have been widely applied to polarimetric synthetic aperture radar (PolSAR) semantic segmentation. However, successive downsampling may lead to the loss of fine spatial details, while conventional U-Net-style decoder directly concatenates encoder features with the corresponding decoder features without explicitly accounting for their semantic discrepancy, potentially introducing redundant or irrelevant information and weakening feature discrimination. To address these limitations, this paper proposes a lightweight complex-valued high-resolution U-Net (CV-HRU-Net) with a complex-valued cross-gated attention (CV-CGA) module for PolSAR semantic segmentation. CV-HRU-Net employs a complex-valued high-resolution network (CV-HRNet) as the encoder to maintain high-resolution representations through parallel multi-resolution streams, while a complex-valued U-Net (CV-U-Net) decoder progressively incorporates multi-resolution high-level semantic features for pixel-wise prediction. To improve encoder–decoder feature interaction, CV-CGA adaptively calibrates the core decoder features using encoder information. Specifically, CV-CGA integrates the Convolutional Block Attention Module, Transformer-style cross-attention with decoder features as queries and encoder features as keys and values, and adaptive gated recalibration to enhance semantic selectivity and boundary representation. Experiments on two airborne and two spaceborne PolSAR datasets demonstrate that the proposed network achieves accurate land-cover segmentation and precise boundary delineation by jointly exploiting polarimetric phase relationships, fine spatial details, and multi-resolution semantic information. Furthermore, CV-CGA substantially improves segmentation accuracy and boundary F1 scores while introducing only marginal model-size overhead. Full article

Reference labels for crop-type mapping are frequently coarse, and administrative land-use registries such as Japan’s eMAF (electronic Map of Agriculture and Forestry) database routinely group agronomically distinct crops under broad, ambiguous categories. This study addresses that problem for the Nakasatsunai region of Hokkaido, Japan, by combining Sentinel-1 synthetic aperture radar (SAR) time series with field-level optical vegetation-index analysis in a modular processing pipeline. The principal novelty of the work is not the pipeline architecture alone but a three-step, Normalized Difference Vegetation Index (NDVI)-driven label-refinement procedure—automatic removal of non-growing or low-amplitude field samples, Euclidean k-means subclass discovery within each coarse label, and trajectory-based label correction—that converts noisy nine-class eMAF labels into a more reliable training set prior to classifier training. The feature set combines the Radar Vegetation Index (RVI), VV and VH backscatter, the γVH/γVV polarization ratio, and NDVI, together with temporal-shape descriptors (phenological timing, peak magnitude, amplitude, maximum slope, and area under the curve) derived from monthly growth trajectories over the 2018 growing season. A Random Forest classifier, together with a gradient-boosting comparator, is evaluated before and after preprocessing under stratified k-fold cross-validation. Across n = 1208 field samples spanning the nine eMAF classes, classification accuracy improved from an overall accuracy of 71.8% on the raw labels to 82.6% after the three-step refinement; Cohen’s kappa increased from 0.63 to 0.77. Correlation analysis indicates that γVH/γVV tracks field-level NDVI more consistently (mean Pearson r = 0.68) than RVI does (mean Pearson r = 0.43) across the eight classes with sufficient samples, motivating its use as a SAR-only phenological proxy; this comparison is extended to the polarimetric PRVI, DPSVI, and DpRVI indices in the discussion. The underlying 80–90% label-accuracy estimate is derived from NDVI trajectory inspection rather than independent, field-surveyed ground truth, and a factorial ablation is used to characterize, to the extent the cross-validated evidence allows, how much of the reported accuracy gain is attributable to label-error correction as opposed to NDVI–SAR feature fusion; both this attribution and the label-accuracy estimate itself are identified as priorities for field validation in future work. The proposed framework is intended to convert coarse, noisy crop labels into a structured and reliable dataset while producing interpretable, field-level phenological insight for agricultural monitoring. Full article

Bio-radar is widely used for casualty search and rescue. However, its short-range and handheld operation mode limits the efficiency of wide-area detection. This paper applies UAV-borne fully polarimetric synthetic aperture radar (PolSAR) to the detection of stationary human targets at long ranges and experimentally validates the feasibility of this framework. To address the difficulty of detecting weakly scattering stationary human targets in strong clutter backgrounds, we propose a novel framework that integrates clutter suppression, target enhancement, and false-alarm suppression. The method introduces, for the first time, polarimetric scattering mechanism analysis into human target enhancement. This improves the SCNR of stationary human targets by approximately 13 dB on average. Then, using neighborhood density features, we reduced the number of non-zero pixels in non-human-target areas (NPINA) by 93.8% and 96.9% in the two passes, respectively. Finally, through dual-pass local binary-map correlation, we obtained the locations of the human targets. This study serves as a proof-of-concept and presents preliminary experimental results on the feasibility of UAV-borne PolSAR for long-range stationary human target detection. The experiment was conducted in a parking lot adjacent to a highway, with three subjects lying supine or prone on a gravel surface, and the scene included buildings such as garages and vegetation such as grass. The experimental results indicate that, under the tested conditions, UAV-borne fully polarimetric SAR is feasible for long-range stationary human target detection. This study expands the application scope of SAR and provides a new technical approach for rapid UAV-based human target detection. Full article

Marine oil spills severely threaten marine ecosystems, the coastal economy, and marine engineering structures. Because it enables all-weather and all-time acquisition of rich target information, fully polarimetric synthetic aperture radar (FP SAR) is widely used for monitoring marine oil spills. However, the differences in the scattering characteristics among oil types can cause variability in the information contained in the features extracted using FP SAR. Herein, RADARSAT-2 images obtained from a rare oil-on-water experiment conducted in the Norwegian North Sea were used to compare the distribution differences in polarimetric features based on the oil slick type and incident angle. Results showed that the incident angle exerted some influence on polarimetric features and the detection performance for oil spills with a low oil–water contrast, particularly at large incident angles. The polarimetric features related to scattering mechanisms exhibited good robustness and effectiveness across various incident angles. The polarimetric feature that combines the scattering entropy H and modified anisotropy A₁₂ exhibited strong overall performance and high suitability for extracting information on oil spills at different incident angles. This study demonstrates that incorporating appropriate polarimetric features according to the incident angle enables the identification of different oil slick types and facilitates oil spill detection and monitoring. 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

Rice cultivation is a major source of methane (CH₄) emission in the agricultural sector, with a significantly higher global warming potential than carbon dioxide. Accurate and scalable quantification of CH₄ from rice paddies is essential for carbon accounting. This study presents an automated framework for estimating rice CH₄ emissions from irrigated paddies in the central plain of Thailand, integrating multi-sensor Synthetic Aperture Radar (SAR) observations with the IPCC methodology. The framework combines Sentinel-1 C-band SAR time series for phenological detection, ALOS-2 PALSAR-2 L-band full-polarimetric SAR for water regime classification, and IPCC water-scaling factors corresponding to Continuous Flooding, Single Drainage, or Multiple Drainage regimes. Evaluated across five stratified holdout sets, the phenology detection algorithm achieved planting and harvesting date Mean Absolute Errors of 6.1 ± 1.4 and 8.3 ± 1.7 days, with a 97.0% ± 2.7% operational detection rate. Water regime classification employed rice growth stage-specific Support Vector Machine classifiers with Radial Basis Function kernels (SVM-RBF), achieving per-stage test Balanced Accuracy ranging from 0.59 to 0.89. End-to-end integration using a four-track counterfactual decomposition yielded a full-pipeline mean absolute error of 18.5 ± 4.5 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$ (21.4% of the mean ground-based CH₄ calculation) and a mean bias of 3.5 ± 5.8 $\mathrm{kg}{\mathrm{CH}}_4{\mathrm{ha}}^{-1}$. Water level classification was confirmed as the dominant algorithmic uncertainty source, while the IPCC Tier 1 emission factor structural range (−32% to +48% of the default) exceeded all algorithmic errors combined. The proposed framework provides a spatially explicit approach for integrating multi-frequency SAR data into IPCC-compliant methane estimation, supporting Monitoring, Reporting, and Verification applications. Full article

Post-disaster scenarios such as tsunamis require rapid terrain assessment that cannot wait for the next satellite synthetic aperture radar (SAR) revisit, yet a readily deployable system remains lacking. We present an off-the-shelf K-band drone-borne dual circular polarimetric (DCP) SAR and a processing pipeline for on-demand terrain classification. Compared with fully polarimetric (FP) SAR, DCP requires only a single transmit polarization and two receive channels, providing a wider swath than FP for the same acquisition, while still separating odd-bounce and even-bounce scattering mechanisms, which dual linear polarimetric modes with the same channel count provide with greater ambiguity due to their sensitivity to target orientation angle. To compensate for platform motion, we implemented RTK global navigation satellite system (GNSS) guided time-domain backprojection (TDBP) with phase gradient autofocus (PGA), yielding an 11.98 dB improvement in peak amplitude. We then applied single-target wire calibration to correct a measured 8.91 dB inter-channel complex gain difference between co-polarization and cross-polarization. As a result, H/α decomposition of the calibrated DCP data classifies canonical reflectors, artificial structures, gravel roads, vegetation, and a pond surface. These field experiments extend compact polarimetric H/α decomposition to drone-borne SAR data for terrain discrimination, establishing a practical pathway toward rapid post-disaster terrain assessment. Full article

Flood-season lake spatiotemporal dynamics are vital for ecological security and socioeconomic development, requiring consistent high-resolution monitoring. However, precipitation fluctuations and sediment turbidity significantly alter water quality, while blurred boundaries between water and floodplain wetlands challenge precise monitoring. To address these issues, this study proposes a water body extraction method leveraging polarimetric Synthetic Aperture Radar data. utilizes the maximum between-class variance algorithm for initial segmentation, optimizes the threshold via a genetic algorithm, and employs dynamic morphological operations to refine boundary details. The method was validated using 2015–2025 Sentinel-1 flood-season time series of Dongting Lake on Google Earth Engine. The results demonstrate that the proposed method achieves stable and accurate water extraction across various years and seasons, with an overall accuracy surpassing 0.93, confirming its robustness and broad applicability. Furthermore, the spatiotemporal hydrodynamics and driving mechanisms of Dongting Lake were analyzed by integrating the extracted water areas with multi-source data, including water level, precipitation, discharge, temperature, and sunshine duration. Findings indicate that the flood-season water area exhibited a fluctuating trend, initially increasing and subsequently decreasing, peaking at 2202.26 km² in 2020 and dropping to 614.04 km² in 2025, a pattern primarily driven by extreme meteorological events such as heavy rainfall and prolonged droughts. Spatially, inundation patterns were characterized by deeper water in the north and shallower depths in the south, separated by a topographically higher central region. Regression analysis revealed a robust correlation between water area and water level with an R² of 0.931, providing a quantitative reference for water level estimation in ungauged regions. Additionally, discharge and precipitation were positively correlated with water area, whereas temperature and sunshine duration exerted a negligible influence. This study supports flood regulation in the Dongting Lake basin and provides a robust framework for analyzing lake dynamics using long-term SAR data. Full article

Polarimetric synthetic aperture radar (Pol-SAR) is one of the most important approaches for sea-surface target detection, but under rainfall conditions it tends to be distorted by the electromagnetic (EM) propagation effects and clutter interference of rainfall. To address this problem, this paper proposes a joint compensation method to mitigate the impacts of rainfall on the detection of sea-surface targets. In the method, a composite imaging model that thoroughly takes into account the propagation and scattering effects of rainfall, sea surface, and ship targets is first established. Then, a range-wise algorithm is proposed to effectively estimate the propagation effects, which are used to compensate for the radar echoes distorted by rainfall. Consequently, a hierarchical search strategy is employed to optimize the receiving polarization state to better discriminate the targets from rainfall and sea clutter. Simulation results show that, across the tested sea-surface wind and rainfall conditions, the proposed method improves the signal-to-clutter-plus-noise ratio (SCNR) by 4 to 13 dB compared with the polarimetric whitening filter, demonstrating its effectiveness under coupled rain–sea conditions. Full article

The Lutan-1 (LT-1) mission is the first civilian L-band differential interferometric synthetic aperture radar (SAR) system in China, with interferometry as its primary application. The system comprises two multi-polarimetric satellites, LT-1A and LT-1B. For the purpose of quantitative application from SAR images of Lutan-1 satellites, the relationship between the SAR image intensity and the backscattering coefficient of ground objects should be established by radiometric calibration. Field radiometric calibration provides absolute calibration constants, but it suffers from beam coverage. Internal on-board calibration, by contrast, tracks relative changes in radiometric performance but cannot yield absolute calibration constants. Therefore, we develop a method that combines on-board internal calibration with field radiometric calibration to monitor the radiometric performance of LT-1 satellites and to analyze the variation patterns revealed by both internal and field calibrations. We monitor the amplitude and phase trend of internal calibration, calculate absolute calibration constants from field calibration, and refine and evaluate the absolute calibration constants. We analyzed the internal calibration data and SAR calibration data of the LT-1 satellite from 2023 to 2025. The results show that the TRMs of the LT-1 satellite exhibit a slight decline over time, and the magnitude of the decrease in LT-1B is greater than that of LT-1A. The slight decrease in internal calibration has not yet led to visible changes in the absolute calibration constant for LT-1A, while the absolute calibration constants decrease slightly for LT-1B. After removing the calibration constant outliers and correcting the gain difference among the beams for the LT-1A satellite, absolute radiometric accuracy is improved from 0.40 dB (1σ) to 0.25 dB (1σ). The absolute radiometric accuracy of the LT-1B satellite is 0.38 dB (1σ). It gives a reference for radiometric performance monitoring of the SAR satellite over a long period. Full article

Coherent space-based InSAR observations over snow- and ice-covered areas have been a valuable resource for cryospheric research. Coherence is considered a critical parameter for evaluating the quality of InSAR observations. This study evaluates the coherence characteristics of snow- and ice-covered areas using mainly fully polarimetric (quad-pol) X-band TerraSAR-X (TSX) and L-band ALOS PALSAR observations. The TSX data were acquired systematically during the Dual Receive Antenna campaign in 2010, while the quad-pol ALOS PALSAR L-band observations were acquired in 2007. A total of 57 TSX quad-pol images acquired over 17 areas at latitudes higher than 60° N were analyzed. The results across all study areas show relatively high coherence levels, ranging from 0.38 to 0.57, with the highest values observed in VV, followed by HH, and the lowest in HV. Interestingly, the highest coherence was found in the VV polarization, whereas HH coherence is typically higher than VV coherence in most InSAR applications. A comparative coherence analysis using quad-pol ALOS PALSAR L-band observations over selected snow- and ice-covered areas showed very similar coherence levels for both HH and VV polarizations. These results suggest that VV polarization is the most suitable for X-band InSAR applications over snow- and ice-covered areas. Full article