Controlled oil-on-water field exercises provide rare opportunities to validate marine pollution monitoring tools under realistic spill conditions. Here we use multi-temporal and multi-channel airborne and spaceborne synthetic aperture radar (SAR) observations from the NORSE experiment and the NOFO oil-on-Water campaigns in the North Sea to study (i) slick evolution and (ii) the separability of hazardous petroleum/mineral oils, including different emulsion states, from biogenic or vegetable look-alike films. The temporal analysis shows that spreading can be rapid at early stages, reinforcing the need for frequent satellite screening during response operations. At the same time, look-alike films can mimic low-backscatter oil signatures, so operational systems must retain enough polarimetric information to reduce false alarms. We therefore develop an unsupervised compact hybrid-polarimetric approach and introduce the Model-based Effective Reflection Coefficient (MERC), a physically motivated descriptor derived from hybrid-polarimetric scattering components. Across both airborne and satellite datasets, MERC improves oil detection and oil-type characterization compared with commonly used intensity and compact-polarimetric descriptors, supporting scalable, wide-swath SAR surveillance. These findings are relevant for routine monitoring of shipping- and offshore-related spills and for Arctic-adjacent waters, where remote location, limited accessibility, and extended periods of darkness increase reliance on all-weather satellite observations.

Electronically switched beamforming antenna array systems offer a simple, low-loss, and energy-efficient alternative for beam steering. To achieve higher beam-steering resolution, the reconfigurable beam switching network (RBSN) has been reported in literature. However, scaling to a larger switched beamforming antenna array system escalates system complexity exponentially. To address this challenge, this paper proposes a streamlined RBSN architecture designed for an $\boldsymbol{N}{\times }\boldsymbol{N}$ Butler matrix-based switched beamforming systems, generating $\boldsymbol{2N-1}$ beams using single-cum-dual-port excitation. As a proof-of-concept, we have implemented the streamlined RBSN architecture using an $\boldsymbol{8\times 8}$ Butler matrix. Additionally, we demonstrated the beam-steering functionality by integrating the streamlined RBSN prototype, including its intermediate phase-shifter stage, with an $\boldsymbol{8\times 8}$ Butler matrix and an $\bf {8}$-element linear antenna array, operating at $\bf {5.8}$ GHz. This system is capable of generating $\bf {15}$ distinct beam-pointing directions through single-cum-dual-port excitation, using a $\bf {14}$-bit coding scheme. The RBSN design is realized on a multilayer printed circuit board, compactly sized at $\boldsymbol{9.6 \times 9.8}$ cm$^{2}$ . The measured results showcase the capability of RBSN to steer beams in the predefined directions with a maximum deviation of $\boldsymbol{\pm 2^{\circ }}$. In order to demonstrate the applicability of the proposed system, an FMCW radar target localization simulation is presented to illustrate the advantage of enhanced beam-steering resolution. Furthermore, a generalized beam-generation formulation is also presented for synthesizing arbitrary additional beam directions between conventional Butler matrix beams leading to continuous beam steering.

Impact craters that stand out as distinctive features on the lunar surface preserve vital evidence on the evolution of lunar surface and its interior. In the absence of a substantial atmosphere, the degradation of lunar craters occurs due to the influence of factors such as gravity, subsequent meteoritic impacts, volcanic events, slope processes, and solar wind. The present study aimed to analyze the morphometry of impact craters in the South Polar region of the lunar surface by utilizing the Dual Frequency Synthetic Aperture Radar (DFSAR) onboard Chandrayaan-2 data. Chandrayaan-2 ' s DFSAR is the world's first fully polarimetric payload, offering data in the L & S-band frequency. The study employed these data, in conjunction with LOLA-DEM, to map and classify craters in the Permanently Shadowed Region (PSR) of the Lunar South Pole based on their shapes and degradation status. Craters were initially mapped using backscatter analysis and scattering components extracted through the Yamaguchi decomposition technique of calibrated DFSAR data. Based on the assessment of different backscatter, and decomposition images a total of 58 craters have been precisely mapped. Additionally, different morphometric analysis of craters such as the computation of Depth/Diameter (d/D) ratio, slope, Circularity Index (CI) and Topographic Roughness Index (TRI) were carried out to understand the degree of degradation and to distinguish them. It was observed that craters with steeper slopes, CI value close to unity, higher d/D ratio and TRI values are preserved, and lower values of these parameters indicated a progression of degradation in the craters. Based on these observations, out of 58 craters, 15 craters were classified as preserved, 29 as partially degraded, and 14 as degraded. The study signifies that DFSAR data can be effectively utilized to study the PSR of the Lunar surface. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Millimetre-wave (mmWave) Frequency-Modulated Continuous-Wave (FMCW) radar systems are increasingly used in imaging, automotive, and industrial applications due to their compact size, high resolution, and robustness to environmental conditions. Linear MIMO radar arrays, while providing excellent angular resolution in one plane (typically azimuth), suffer from poor resolution in the orthogonal (elevation) direction due to limited aperture extent. In this work, we propose a general and hardware-efficient method to overcome this limitation by acquiring radar measurements at two orthogonal orientations using a simple mechanical rotation. The horizontal and vertical acquisitions offer complementary spatial resolution, and their coherent fusion yields a more isotropic point spread function (PSF), enabling improved 2D imaging quality and target separability. The proposed technique does not require any hardware modification and is applicable to a wide range of mmWave MIMO radar systems. Experimental validation is performed using a cascaded $4 \times$ AWR2243 FMCW radar platform, demonstrating the effectiveness of the approach in achieving high-resolution 2D imaging with minimal complexity.

Three-Dimensional Interferometric Synthetic Aperture Radar (3D-InISAR) imaging provides a more complete and reliable representation of targets compared to traditional 2D-ISAR, overcoming limitations related to the geometry of the radar-target system and relative motion. This paper presents the application of a Point Cloud Transformer (PCT) for Automatic Target Recognition (ATR) using 3D-InISAR data. The PCT model, originally developed to classify LIDAR’s point clouds, is trained on sparse synthetic point cloud datasets representing various military vehicles, including cars, tanks, and trucks. The synthetic data is carefully generated from CAD models, incorporating techniques such as voxel downsampling and data augmentation to ensure high fidelity and diversity. Initial testing on synthetic data demonstrates the PCT’s robustness and high accuracy when used for ATR. To bridge the gap between synthetic and real data, a transfer learning approach is employed, which operates a fine-tuning on the pre-trained model by using real 3D-InISAR point clouds obtained from the publicly available SDMS-AFRL dataset. Results show significant improvements in classification accuracy post fine-tuning, validating the effectiveness of the PCT model for real-world ATR applications. The findings highlight the potential of transformer-based models in enhancing target recognition systems for future ATR systems based on 3D radar images.

Synthetic Aperture Radar (SAR) is capable of penetrating clouds, fog, and adverse weather conditions, enabling continuous, all-weather monitoring. When deployed on satellite platforms, SAR becomes particularly valuable for observing remote regions like the Arctic. With increasing global interest in Arctic shipping routes, the risk of oil spills is expected to rise significantly. However, the region’s remoteness and lack of critical infrastructure make spaceborne sensors the most viable monitoring solution. SAR plays a crucial role in accurate oil spill detection while minimizing false alarms through various polarimetric configurations. Among these, hybrid-polarimetry (hybrid-pol) SAR stands out as it acquires sufficient polarimetric information for precise detection with wide swath coverage, ensuring shorter satellite revisit time. This study demonstrates the effectiveness of hybrid-pol SAR not only in detecting oil spills but also in characterizing them as harmful or non-harmful.

The growing population of space debris poses significant risks to operational satellites and future space missions, necessitating innovative and efficient tracking solutions. Ground-based radar for space surveillance has been a central area of research since the early Space Age, with recent advancements emphasizing the use of bistatic radar systems that incorporate sensitive radio telescopes as receivers. This approach offers a cost-effective and scalable solution for monitoring space debris. Preliminary observations demonstrated the viability of employing radio telescopes in bistatic configurations for effective debris tracking. This review provides a comprehensive analysis of experiments utilizing radio telescopes as bistatic receivers, highlighting key advancements, challenges, and potential applications in space surveillance systems. By detailing the progress in this field, this study underscores the critical role of bistatic radar systems in mitigating the growing threat of space debris.

Global warming, leading to the melting of ice in the Arctic Polar regions, has opened the possibility of utilizing the Northeast and Northwest Passages. These routes can significantly reduce travel time and fuel consumption for cargo ships transporting goods worldwide. However, this also increases the risk of oil spills in the region. Since the Arctic is remote and lacks significant infrastructure, satellite datasets provide the most viable solution for monitoring oil spills. The key parameters to consider are the availability of sufficient information for accurate detection and the shortest possible satellite revisit time. Hybrid-polarimetry SAR satellite sensors offer these capabilities. This paper also explores the potential of hybrid-pol SAR data not only for detecting oil spills but also for characterizing them as harmful or non-harmful in the region.

The inverse synthetic aperture radar (ISAR) system exploits the movement of the target to form its high-resolution image. Further, the multi-polarisation acquisition in ISAR collects additional information on the target's scattering properties and surface characteristics that help to enhance the imaging capabilities of ISAR. In this study, we suggest a novel multi-polarisation ISAR configuration based on the circular transmit and linear receive (CTLR) combination, namely CTLR hybrid-pol ISAR, for the application of non-cooperative target detection and imaging. The CTLR hybrid-pol ISAR captures sufficient information about the targets to accurately characterise them, and simultaneously overcomes the drawbacks of full-polarimetry (full-pol) ISAR associated with the transmission of two pulses to obtain a single unit of polarimetric back-scattered information. Validation is performed using real ISAR data of a T-72 tank target, collected under the moving and stationary target acquisition and recognition (MSTAR) programme conducted by the Georgia Tech Research Institute. A comparative analysis based on SPAN, entropy, and polarimetric decomposition is carried out between the full-pol ISAR and CTLR hybrid-pol ISAR information. The results conclude that CTLR hybrid-pol ISAR maintains a similar level of information content compared to full-pol ISAR while overcoming its drawbacks.

This manuscript presents a novel method for enhancing image quality of non-cooperative targets in Synthetic Aperture Radar (SAR) imaging. The approach leverages Inverse SAR (ISAR) processing to refine electromagnetic radar imagery of moving targets within SAR scenes, effectively overcoming limitations associated with the commonly used Range-Doppler (RD) image formation technique. This is particularly timely, given the capabilities of emerging satellite systems such as COSMO Second Generation and ICEYE, which support longer coherent processing intervals (CPI), thereby increasing the relevance and effectiveness of the proposed method. To evaluate its performance, we applied the technique to a highly maneuvering ship extracted from a COSMO-SkyMed (CSK) SAR image. Results demonstrate a marked improvement in image contrast and a reduction in image entropy, confirming the method's ability to produce sharper and more focused imagery.

The use of scattering models with dielectric surfaces in hybrid-polarimetry (hybrid-pol) synthetic aperture radar (SAR) decomposition methods offers a more realistic representation of land-cover surfaces. Additionally, redefining an appropriate volume scattering model improves classification accuracy, particularly in complex urban areas where correctly classifying pixels in the double-bounce (DB) scattering mechanism category is challenging. In this letter, a prominent volume scattering model is incorporated into the hybrid-pol three-component scattering model (HTM) framework. In this investigation, the proposed algorithm is referred to as "Modified HTM." The proposed method is based on the Neumann volume scattering model (NVSM). Given the complex polarimetric radar responses from vegetation, NVSM has been demonstrated to be an optimal parameter inversion framework that is capable of efficiently recover the spatial distribution and scattering properties from the volumetric media. To present the improved efficacy, the proposed technique is compared to the conventional hybrid-pol techniques, specifically, the m-delta , m-alpha , and original HTM decomposition. An assessment based on ship detection has also been included to illustrate its efficacy.

Pulse-to-Pulse Phase Correction for Inverse Synthetic Aperture Radar (PPPC-ISAR) method, designed to enhance SAR imaging of non-cooperative targets exhibiting complex motion, is presented in this paper. The proposed approach builds upon the closed-form PPPC method originally introduced for stationary-target SAR applications and extends it, for the first time, to the ISAR framework. Unlike conventional SAR, where platform motion is controlled and the target is static, ISAR scenarios involve unknown and irregular target maneuvers, necessitating a fundamentally different correction strategy. The PPPC-ISAR method is integrated into a complete ISAR processing pipeline, including target detection in the SAR scene, echo extraction, autofocusing, and final image formation using the Back-Projection ISAR (BP-ISAR) technique. The use of backprojection provides access to pulse-by-pulse data, which is wellsuited for implementing the proposed phase correction at an individual pulse level. Additionally, computational improvements are introduced to enhance the efficiency of the overall methodology. The effectiveness of the proposed method is validated using COSMO-SkyMed satellite SAR data.

In the existing literature, the linear cross-polarization term, i.e. <|SHV|(2)>, is often considered a lost parameter in hybrid-polarimetry (hybrid-pol) SAR. This hypothesis arose from the recognition that, even when taking into account the reflection-symmetry condition (which is nearly valid for all types of natural surfaces), there are five parameters required to compute <|SHV|(2)>, whereas Hybrid-pol SAR can only acquire four parameters, resulting in an underdetermined solution. We respectfully hold a different perspective from the earlier statements and would like to clarify that utilizing four parameters, instead of five, is indeed adequate for the precise calculation of <|SHV|(2)>. Consequently, we have established a mathematical relationship under the reflection symmetry condition that provides a closed-form solution for calculating <|SHV|(2)> from hybrid-pol SAR datasets. For validation, both theoretical and practical assessments have been conducted, with the implementation of compound scattering matrices based analysis and the real Satellite ALOS PALSAR L-band dataset.

In the current literature, the linear cross-polarization term, denoted as , is often considered a lost parameter in hybrid-polarimetry (hybrid-pol) SAR. This assumption is based on the belief that even with the reflection-symmetry condition (which holds for most natural surfaces), five parameters are needed to compute , whereas hybrid-pol can only measure four parameters, leading to an underdetermined solution. We respectfully disagree with this viewpoint and assert that four parameters are indeed sufficient for accurately calculating . Accordingly, we have derived a mathematical relationship under the reflection symmetry condition that provides a closed-form solution for calculating from hybrid-pol SAR datasets. This has been validated through both theoretical analysis and practical assessments using ALOS PALSAR L-band data

Polarimetric Inverse Synthetic Aperture Radar (PolISAR) images have been exploited for automatic target recognition (ATR) and classification due to their rich and detailed information. However, the performance of Pol-ISAR image classification systems can be degraded by adversarial attacks, which are imperceptible perturbations introduced into the input data to deceive the classifier. In this paper, we first examine the impact of adversarial perturbations on the explainability of the classification process. In particular, we employ the Local Interpretable Model-Agnostic Explanations (LIME) method to explain the feature importance of a convolutional neural network (CNN) under adversarial perturbations generated by the fast gradient sign method (FGSM). By comparing the LIME explanation under different perturbation levels with that of the non-perturbed scenario, we propose a numerical metric called the LIME Consistency Score (LCS) to assess the consistency of LIME explanations across various levels of perturbation. We then examine how this score aligns with the CNN's decision. Additionally, we propose an ensemble learning strategy with different architectures and loss functions to improve the resilience of Pol-ISAR-ATR against adversarial examples (AEs) and reduce their transferability. We conduct our experiments on a Pol-ISAR dataset of a T72 tank, which is converted to 3-channel data using Pauli's decomposition. The results demonstrate the effectiveness and potential of our proposed framework.

A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3D imaging method for non-cooperative targets is proposed in this paper. 3D imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2D ISAR imaging. In the previously reported single-polarimetry InISAR based 3D imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3D ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3D imaging of the target is possible. To validate this proposed Pol-InISAR based 3D imaging approach, both simulated and real datasets are taken under consideration.

The Inverse Synthetic Aperture Radar (ISAR) exploits the movement of the target to form high-resolution imaging of the target. Further, the multi-polarization acquisition in ISAR gathers additional information on the target’s scattering properties and surface characteristics that help to enhance the imaging capabilities of ISAR. In this manuscript, we suggest a novel multi-polarization ISAR configuration based on the Circular Transmit and Linear Receive (CTLR) combination, namely hybrid-pol ISAR, for the application of non-cooperative target detection and imaging. Hybrid-pol ISAR can capture sufficient information about the targets to characterize them properly, and simultaneously overcomes the demerits of full-polarimetric (full-pol) ISAR associated with the transmission of two pulses to capture a single unit of polarimetric back-scattered information. The real Tank-72 ISAR data is implemented for the validation, which was captured under the MSTAR program by Georgia Technology Research Institute (GTRI).

In the application of oil-spill monitoring, the satellite revisit time needs to be as short as possible to identify minor spills before they can cause widespread damage. Simultaneously, it is required to capture a sufficient amount of information about the surface to clearly distinguish between oil-spilled and oil-free sea regions. The hybrid-polarimetry (hybrid-pol) synthetic aperture radar (SAR) system can be exploited for such capabilities. However, limited hybrid-pol-based oil-spill descriptors are reported in the literature in comparison with rich sets of full-polarimetry (full-pol)-based descriptors. In this letter, we establish a direct relation between hybrid-pol data and full-pol data under reflection-symmetry condition. Consequently, through the proposed work, the rich sets of full-pol-based oil-spill descriptors can be derived directly from the hybrid-pol datasets. For the validation of the proposed work, L-band ALOS PALSAR and UAVSAR datasets acquired over the Gulf of Mexico have been used.

2D-ISAR produces images that strictly depend on the geometry of the whole radar-target system and on the relative motion between radar and target. This poses some limits on the use of Automatic Target Recognition (ATR) systems. To overcome this issue, 3D point clouds as a result of 3D-ISAR imaging were proposed as a more complete and reliable representation of the target. Since the acquisition system will output an unknown number of points in a random order, the chosen classifier must be able to process a variable number of input elements to correctly classify the target. After a brief presentation of the state of the art about the 3D classification problem, the architecture of Point Cloud Transformer (PCT) is introduced. PCT is trained and tested on an ad-hoc generated 3D dataset, which in this preliminary experiment contains three different target types: cars, tanks and military trucks. The goal of this work is to show how the transformer is able to correctly manage the recognition of targets, even if the point clouds are made by few points. Lastly, the trained network is tested on some real data.