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This article is a guest editorial for the IET Radar, Sonar & Navigation journal, focusing on selected papers from the RADAR 2022 conference held in Edinburgh, UK. The conference provided an opportunity for radar specialists from 22 countries to explore the latest developments in radar systems. Key topics discussed at the conference included new radar trends, target detection (with a focus on drones), low-frequency radar, and cognitive radar. The special issue contains 17 papers based on extended work presented at the conference, covering topics such as multistatic radar, passive radar, target signatures, and advanced radar processing techniques. The authors hope that this special issue will serve as a valuable resource for further research in the field. [Extracted from the article]

Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target interpretation. With the rapid increase in PolSAR resolution, SAR images in complex natural and artificial scenes exhibit non-homogeneous characteristics, which creates an urgent demand for high-resolution PolSAR filters. To address these issues, a new adaptive PolSAR filter based on joint similarity measure criterion (JSMC) is proposed in this paper. Firstly, a scale-adaptive filtering window is established in order to preserve the texture structure based on a multi-directional ratio edge detector. Secondly, the JSMC is proposed in order to accurately select homogeneous pixels; it describes pixel similarity based on both space distance and polarimetric distance. Thirdly, the homogeneous pixels are filtered based on statistical averaging. Finally, the airborne and spaceborne real data experiment results validate the effectiveness of our proposed method. Compared with other filters, the filter proposed in this paper provides a better outcome for PolSAR data in speckle suppression, edge texture, and the preservation of polarimetric properties. [ABSTRACT FROM AUTHOR]

Millimeter wave operates within a wavelength range of 1mm to 10mm and can penetrate through various non-metallic materials, such as the paperboard and plastic films commonly used in cigarette packaging boxes. In comparison to X-ray devices, millimeter wave imagers have the added advantage of not emitting ionizing radiation and having lower electromagnetic radiation output than the standard limit for mobile phone. Given these characteristics, we believe that utilizing millimeter wave imaging technology for detecting missing carton in packaged cigarette boxes could be a viable solution. The paper presents the fundamental imaging theory and showcases some images of cigarette boxes. The results demonstrate that our millimeter wave imager can produce clear images of packaged cigarette boxes with missing carton. We anticipate that this system has significant potential for application in machine vision for tobacco and other similar industries. [ABSTRACT FROM AUTHOR]

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as "datum", "image", and "detection" suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with "image analysis and classification research" as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges in achieving accurate stereo SAR image matching. To address these aforementioned challenges, this paper proposes a radargrammetric method based on the composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged using the proposed radargrammetric 3D imaging method, resulting in the 3D imaging of target scenes based on multi-aspect SAR images. For validation purposes, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency, while enhancing the imaging accuracy of crucial areas with limited data. [ABSTRACT FROM AUTHOR]

For visible and Synthetic Aperture Radar (SAR) image fusion, this paper proposes a visible and SAR image fusion algorithm based on a Transformer and a Convolutional Neural Network (CNN). Firstly, in this paper, the Restormer Block is used to extract cross-modal shallow features. Then, we introduce an improved Transformer–CNN Feature Extractor (TCFE) with a two-branch residual structure. This includes a Transformer branch that introduces the Lite Transformer (LT) and DropKey for extracting global features and a CNN branch that introduces the Convolutional Block Attention Module (CBAM) for extracting local features. Finally, the fused image is output based on global features extracted by the Transformer branch and local features extracted by the CNN branch. The experiments show that the algorithm proposed in this paper can effectively achieve the extraction and fusion of global and local features of visible and SAR images, so that high-quality visible and SAR fusion images can be obtained. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) is an active microwave sensor that has attracted widespread attention due to its ability to observe the ground around the clock. Research on multi-scale and multi-category target detection methods holds great significance in the fields of maritime resource management and wartime reconnaissance. However, complex scenes often influence SAR object detection, and the diversity of target scales also brings challenges to research. This paper proposes a multi-category SAR image object detection model, CCDS-YOLO, based on YOLOv5s, to address these issues. Embedding the Convolutional Block Attention Module (CBAM) in the feature extraction part of the backbone network enables the model's ability to extract and fuse spatial information and channel information. The 1 × 1 convolution in the feature pyramid network and the first layer convolution of the detection head are replaced with the expanded convolution, Coordinate Conventional (CoordConv), forming a CRD-FPN module. This module more accurately perceives the spatial details of the feature map, enhancing the model's ability to handle regression tasks compared to traditional convolution. In the detector segment, a decoupled head is utilized for feature extraction, offering optimal and effective feature information for the classification and regression branches separately. The traditional Non-Maximum Suppression (NMS) is substituted with the Soft Non-Maximum Suppression (Soft-NMS), successfully reducing the model's duplicate detection rate for compact objects. Based on the experimental findings, the approach presented in this paper demonstrates excellent results in multi-category target recognition for SAR images. Empirical comparisons are conducted on the filtered MSAR dataset. Compared with YOLOv5s, the performance of CCDS-YOLO has been significantly improved. The mAP@0.5 value increases by 3.3% to 92.3%, the precision increases by 3.4%, and the mAP@0.5:0.95 increases by 6.7%. Furthermore, in comparison with other mainstream detection models, CCDS-YOLO stands out in overall performance and anti-interference ability. [ABSTRACT FROM AUTHOR]

Rapid mapping demands efficient methods for a fast extraction of information from satellite data while minimizing data requirements. This paper explores the potential of deep learning for the generation of high-resolution urban elevation data from Synthetic Aperture Radar (SAR) imagery. In order to mitigate occlusion effects caused by the side-looking nature of SAR remote sensing, two SAR images from opposing aspects are leveraged and processed in an end-to-end deep neural network. The presented approach is the first of its kind to implicitly handle the transition from the SAR-specific slant range geometry to a ground-based mapping geometry within the model architecture. Comparative experiments demonstrate the superiority of the dual-aspect fusion over single-image methods in terms of reconstruction quality and geolocation accuracy. Notably, the model exhibits robust performance across diverse acquisition modes and geometries, showcasing its generalizability and suitability for height mapping applications. The study's findings underscore the potential of deep learning-driven SAR techniques in generating high-quality urban surface models efficiently and economically. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites. [ABSTRACT FROM AUTHOR]

Complex geometric distortions and nonlinear radiation differences between optical and synthetic aperture radar (SAR) images present challenges for the matching of sufficient and evenly distributed corresponding points. To address this problem, this paper proposes a deep convolutional network based on an attention mechanism for matching optical and SAR images. In order to obtain robust feature points, we employ phase consistency instead of image intensity and gradient information for feature detection. A deep convolutional network (DCN) is designed to extract high-level semantic features between optical and SAR images, providing robustness to geometric distortion and nonlinear radiation changes. Notably, incorporating multiple inverted residual structures in the DCN facilitates efficient extraction of local and global features, promoting feature reuse, and reducing the loss of key features. Furthermore, a dense feature fusion module based on coordinate attention is designed, focusing on the spatial positional information of effective features, integrating key features into deep descriptors to enhance the robustness of deep descriptors to nonlinear radiometric differences. A coarse-to-fine strategy is then employed to enhance accuracy by eliminating mismatches. Experimental results demonstrate that the proposed network performs better than the manually designed descriptors-based methods and the stateof- the-art deep learning networks in both matching effectiveness and accuracy. Specifically, the number of matches achieved is approximately 2 times greater than that of other methods, with a 10% improvement in F-measure. [ABSTRACT FROM AUTHOR]

High-resolution and high-squint spaceborne spotlight synthetic aperture radar (SAR) has significant potential for extensive application in remote sensing, but its swath width effectiveness is constrained by a critical factor: severe range cell migration (RCM). To address this, pulse repetition interval (PRI) variation offers a practical scheme for raw data reception. However, the current designs for continuously varying PRI (CV-PRI) exhibit high complexity in engineering. In response to the issue, this paper proposes a novel strategy of stepwise varying PRI (SV-PRI), which demonstrates higher reconstruction accuracy compared with CV-PRI. Furthermore, confronting the azimuth non-uniform sampling characteristics induced by the PRI variation, this paper introduces a complete uniform reconstruction processing based on the azimuth partitioning methodology, which effectively alleviates the inherent contradiction between resolution and swath width. The processing flow, utilizing the temporal point remapping (TPR) concept, ensures the uniformity and coherence of dataset partitioning and reassembly in the context of the interpolation on non-uniform grids. Finally, according to the simulation results, the point target data, processed through the processing flow proposed in this study, have demonstrated effective focusing results. [ABSTRACT FROM AUTHOR]

This document explores the significance of remote sensing in monitoring maritime activities and identifying vessels. It emphasizes the need for surveillance to ensure safety, security, and emergency management, given the increasing number of vessels worldwide. The document highlights the use of technologies like the Automatic Identification System (AIS) and remote sensing in situations where collaborative systems are not reliable. It also discusses the integration of data from different sensors and the application of data science techniques for a comprehensive assessment of maritime traffic. The document concludes by summarizing research papers on ship detection, tracking, and classification using various sensors and data processing techniques. [Extracted from the article]

Introduction: Optical and SAR image matching is one of the fields within multisensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images. Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network. Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice. [ABSTRACT FROM AUTHOR]

When Synthetic Aperture Radar (SAR) observes complex structural targets such as oil tanks, it is easily interfered with by multipath signals, resulting in a large number of multipath ghost images in the SAR image, which seriously affect the image clarity. To address this problem, this paper proposes a multi-aspect multipath suppression method. This method observes complex structural targets from different azimuth angles to obtain a multi-aspect image sequence and then uses the difference in sequence features between the target image and the multipath ghost image with respect to aspect angle to separate them. This paper takes a floating-roof oil tank as an example to analyze the propagation path and the ghost image characteristics of multipath signals under different observation aspects. We conclude that the scattering center of the multipath ghost image changes with the radar observation aspect, whereas the scattering center of the target image does not. This paper uses the Robust Principal Component Analysis (RPCA) method to decompose the image sequence matrix into two parts: a sparse matrix and a low-rank matrix. The low-rank matrix represents the aspect-stable principal component in the image sequence; that is, the real scattering center. The sparse matrix represents the part of the image sequence that deviates from the principal component; that is, the signal that varies with aspect, mainly including multipath signals, sidelobes, anisotropic signals, etc. By reconstructing the low-rank matrix and the sparse matrix, respectively, we can obtain the image after multipath signal suppression and also the multipath ghost image. Both the target and the multipath signal provide useful information. The image after multipath signal suppression is useful for obtaining the structural information of the target, and the multipath ghost image is useful for analyzing the multipath phenomenon of the complex structure target. This paper conducts experimental verification using real airborne SAR data of an external floating roof oil tank and compares three methods: RPCA, PCA, and sub-aperture fusion method. The experiment shows that the RPCA method can better separate the target image and the multipath ghost image. [ABSTRACT FROM AUTHOR]

Circular synthetic aperture radar (CSAR) possesses the capability of multi-angle observation, breaking through the geometric observation constraints of traditional strip SAR and holding the potential for three-dimensional imaging. Its sub-wavelength level of planar resolution, resulting from a long synthetic aperture, makes CSAR highly valuable in the field of high-precision mapping. However, the motion geometry of CSAR is more intricate compared to traditional strip SAR, demanding high precision from navigation systems. The accumulation of errors over the long synthetic aperture time cannot be overlooked. CSAR exhibits significant coupling between the range and azimuth directions, making traditional motion compensation methods based on linear SAR unsuitable for direct application in CSAR. The dynamic nature of flight, with its continuous changes in attitude, introduces a significant deformation error between the non-rigidly connected Inertial Measurement Unit (IMU) and the Global Positioning System (GPS). This deformation error makes it difficult to accurately obtain radar position information, resulting in imaging defocus. The research in this article uncovers a correlation between the deformation error and radial acceleration. Leveraging this insight, we propose utilizing radial acceleration to estimate residual motion errors. This paper delves into the analysis of Position and Orientation System (POS) errors, presenting a novel high-resolution CSAR motion compensation method based on airborne platform acceleration information. Once the system deformation parameters are calibrated using point targets, the deformation error can be directly calculated and compensated based on the acceleration information, ultimately resulting in the generation of a high-resolution image. In this paper, the effectiveness of the method is verified with airborne flight test data. This method can compensate for the deformation error and effectively improve the peak sidelobe ratio and integral sidelobe ratio of the target, thus improving image quality. The introduction of acceleration information provides new means and methods for high-resolution CSAR imaging. [ABSTRACT FROM AUTHOR]

This paper investigates a real-time process generator of wideband signals, which calculates waveforms in a field-programmable gate array (FPGA) using the high-level synthesis (HLS) method. To obtain high-resolution and wide-swath images, the generator must produce multiple modes of large time-bandwidth product (TBP) linear frequency modulation (LFM) signals. However, the conventional storage method is unrealistic as it requires huge storage resources to save pre-computed waveforms. Therefore, this paper proposes a novel processing approach that calculates waveforms in real-time based simply on parameters such as the sampling frequency, bandwidth, and time width. Additionally, this paper implements predistortion through the polynomial curve to approximate phase errors of the system. The parallelizing process in the FPGA is necessary to satisfy the high-speed requirement of a digital-to-analog converter (DAC); however, repeatedly multiplexing real-time calculation consumes extensive logic and DSP resources, potentially exceeding FPGA limitations. To address this, this paper proposes a piecewise linear algorithm to conserve resources, which processes the polynomial only once, acquires the difference in two adjacent values through the register and pipeline, and then adds this increment to facilitate parallel computations. The performance of this proposed generator is validated through simulation and implemented in experiments with an X-band airborne SAR system. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) started as a radio echo sounding technology during the second half of the last century, but it is now a well-established and widely adopted technology for producing high-resolution images of subsurface. Novel processing schemes, including full waveform inversion and machine learning, advanced GPR transmission, and elastic wave methods, are among the research topics regarded as fundamental for the future. By taking into account the UWB nature of GPR methodology, as well as the experienced inefficiencies in traditional design solutions, this paper provides a robust ground for evaluating the optimal choice for GPR system design. [Extracted from the article]

This work proposes a new compact triple-band triangular patch antenna for RF energy harvesting applications in IoT devices. It is realized on Teflon glass substrate with a thickness of 0.67 mm and a relative permittivity of 2.1. Four versions of this antenna have been designed and realized with inclinations of 0°, 30°, 60° and 90° to study the impact of the tilting on their characteristics (S11 parameter, radiation pattern, gain) and to explore the possibilities of their implementation in the architectures of electronic equipment according to the available space. The antenna is also realized on waterproof paper with a thickness of 0.1 mm and a relative permittivity of 1.4 for biomedical domain. All the antennas (vertical antenna, tilted antennas and antenna realized on waterproof paper) have a size of 39 × 9 mm2 and cover the 2.45 GHz and 5.2 GHz Wi-Fi bands and the 8.2 GHz band. A good agreement is obtained between measured and simulated results. Radiation patterns show that all the antennas are omnidirectional for 2.45 GHz and pseudo-omnidirectional for 5.2 GHz and 8.2 GHz with maximum measured gains of 2.6 dBi, 4.55 dBi and 6 dBi, respectively. The maximum measured radiation efficiencies for the three antenna configurations are, respectively, of 75%, 70% and 72%. The Specific Absorption Rate (SAR) for the antenna bound on the human body is of 1.1 W/kg, 0.71 W/kg and 0.45 W/kg, respectively, for the three frequencies 2.45 GHz, 5.2 GHz and 8.2 GHz. All these antennas are then applied to realize RF energy harvesting systems. These systems are designed, realized and tested for the frequency 2.45 GHz, −20 dBm input power and 2 kΩ resistance load. The maximum measured output DC power is of 7.68 µW with a maximum RF-to-DC conversion efficiency of 77%. [ABSTRACT FROM AUTHOR]

Time series dynamic prediction of surface deformation in mining areas can provide reference data for coal mine safety and production, which has important impacts. The combination of interferometric synthetic aperture radar (InSAR) technology and the probability integral method (PIM) is commonly used for predicting deformation. However, most surface subsidence prediction in mining areas is based on the static PIM parameters, failing to achieve the three-dimensional (3D) dynamic deformation prediction. This paper proposed a 3D deformation dynamic prediction model (InSAR-3D-CTPIM) between InSAR deformation observations and dynamic coordinate-time PIM (CTPIM) parameters, which can realize the prediction of east–west, north–south, and vertical series deformation caused by mining. The method has been validated by simulation experiments and real experiments in the mining area of Jiansheng Coal Mine in Baisha Reservoir, Henan Province, China. The results showed that the modeling accuracy was improved by 34.3% compared to the traditional multi-rate model, and the accuracy was improved by 28.5% compared to the vertical deformation obtained by the traditional static PIM method. The InSAR-3D-CTPIM model can be used to predict the evolutionary history of basin-wide surface deformation dynamics in coal mining areas, and provide a reference for the early warning and prediction of geological hazards in coal mining areas. [ABSTRACT FROM AUTHOR]

Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R 2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has found widespread application in SAR image object detection. However, when detecting multi-scale targets against complex backgrounds, these models often struggle to strike a balance between accuracy and speed. Furthermore, there is a continuous need to enhance the performance of current models. Hence, this paper proposes LRMSNet, a new multi-scale target detection model designed specifically for SAR images in complex backgrounds. Firstly, the paper introduces an attention module designed to enhance contextual information aggregation and capture global features, which is integrated into a backbone network with an expanded receptive field for improving SAR image feature extraction. Secondly, this paper develops an information aggregation module to effectively fuse different feature layers of the backbone network. Lastly, to better integrate feature information at various levels, this paper designs a multi-scale aggregation network. We validate the effectiveness of our method on three different SAR object detection datasets (MSAR-1.0, SSDD, and HRSID). Experimental results demonstrate that LRMSNet achieves outstanding performance with a mean average accuracy (mAP) of 95.2%, 98.9%, and 93.3% on the MSAR-1.0, SSDD, and HRSID datasets, respectively, with only 3.46 M parameters and 12.6 G floating-point operation cost (FLOPs). When compared with existing SAR object detection models on the MSAR-1.0 dataset, LRMSNet achieves state-of-the-art (SOTA) performance, showcasing its superiority in addressing SAR detection challenges in large-scale complex environments and across various object scales. [ABSTRACT FROM AUTHOR]

The PIESAT-01 constellation is the world's first multi-baseline distributed synthetic aperture radar (SAR) constellation with a "Cartwheel" formation. The "Cartwheel" formation is a unique formation in which four satellites fly in companion orbits, ensuring that at any given moment, the main satellite remains at the center, with three auxiliary satellites orbiting around it. Due to this unique configuration of the PIESAT-01 constellation, four images of the same region and six pairs of baselines can be obtained with each shot. So far, there has been no imaging and interference research based on four-satellite constellation measured data, and there is an urgent need to explore algorithms for the "Cartwheel" configuration imaging and digital surface model (DSM) production. This paper introduces an improved bistatic SAR imaging algorithm under the four-satellites interferometric mode, which solves the problem of multi-orbit nonparallelism in imaging while ensuring imaging coherence and focusing ability. Subsequently, it presents an interferometric processing method for the six pairs of baselines, weighted fusion based on elevation ambiguity from different baselines, to obtain a high-precision DSM. Finally, this paper selects the Dingxi region of China and other regions with diverse terrains for imaging and DSM production and compares the DSM results with ICESat-2 global geolocated photon data and TanDEM DSM data. The results indicate that the accuracy of PIESAT-01 DSM meets the standards of China's 1:50,000 scale and HRTI-3, demonstrating a high level of precision. Moreover, PIESAT-01 data alleviate the reliance on simulated data for research on multi-baseline imaging and multi-baseline phase unwrapping algorithms and can provide more effective and realistic measured data. [ABSTRACT FROM AUTHOR]

Phase unwrapping of high phase noise and steep phase gradient has always been a challenging problem in interferometric synthetic aperture radar (InSAR), in which case the least squares (LS) phase unwrapping method often suffers from significant unwrapping errors. Therefore, this paper proposes an improved LS phase unwrapping method based on the Chebyshev filter, which solves the problem of incomplete unwrapping and errors under high phase noise and steep phase gradient. Firstly, the steep gradient phase is transformed into multiple flat gradient phases using the Chebyshev filter. Then the flat gradient phases are unwrapped using the LS unwrapping method. Finally, the final unwrapped phase is obtained by iteratively adding the unwrapping results of the flat gradient phases. The simulation results show that the proposed method has the best accuracy and stability compared to LS, PCUA, and RPUA. In the real InSAR phase unwrapping experiment, the RMSE of the proposed method is reduced by 63.91%, 35.38%, and 54.39% compared to LS, PCUA, and RPUA. The phase unwrapping time is reduced by 62.86% and 11.64% compared to PCUA and RPUA. [ABSTRACT FROM AUTHOR]

For synthetic aperture microwave radiometers, the problem of Radio Frequency Interference (RFI) is becoming more and more serious, which affects both the scientific retrieval of remote sensing data and the imaging quality of brightness temperature (BT) images. In the visibility data domain, the array factor synthesis algorithm is commonly employed to mitigate RFI sources and their Gibbs trailing. In the BT image domain, the CLEAN algorithm is typical applied to mitigate RFI sources and their Gibbs trailing. However, the array factor synthesis algorithm can result in anomalous BT points near the "zero trap" region, and the CLEAN algorithm will miss some BT points below a certain threshold. In this paper, a Bi-domain combined mitigation algorithm is proposed to mitigate RFI sources and their Gibbs trailing. Following initial mitigation in the visibility data domain, dual thresholds are applied to normalize anomalous BT points near the "zero trap" region, thereby enhancing imaging quality. The effectiveness of the Bi-domain combined mitigation algorithm is verified by using both measured data from SMOS L1A and simulated data. The experimental results demonstrate that the Bi-domain combined mitigation algorithm is superior to the array factor synthesis algorithm and the CLEAN algorithm in mitigating RFI sources and their Gibbs trailing. [ABSTRACT FROM AUTHOR]

With the number of new satellites increasing dramatically, comprehensive space surveillance is becoming increasingly important. Therefore, high-resolution inverse synthetic aperture radar (ISAR) imaging of satellites can provide an in-situ assessment of the satellites. This paper demonstrates that pseudo-noise signals can also be used for satellite imaging, in addition to classical linear frequency-modulated chirp signals. Pseudo-noise transmission signals offer the advantage of very low cross-correlation values. This, for instance, enables the possibility of a system with multiple channels transmitting instantaneously. Furthermore, it can significantly reduce signal interference with other systems operating in the same frequency spectrum, which is of particular interest for high-bandwidth, high-power systems such as satellite imaging radars. A new routine has been introduced to generate a wideband pseudo-noise signal with a peak-to-average power ratio (PAPR) similar to that of a chirp signal. This is essential for applications where the transmit signal power budget is sharply limited by the high-power amplifier. The paper presents both theoretical descriptions and analysis of the generated pseudo-noise signal as well as the results of an imaging measurement of a real space target using the introduced pseudo-noise signals. [ABSTRACT FROM AUTHOR]

In order to improve the classification performance of Polarimetric Synthetic Aperture Radar (PolSAR) image by synthesizing various polarimetric features, a supervised manifold learning method is proposed in this paper for PolSAR feature extraction and classification. Under the umbrella of tensor algebra, the proposed method characterizes each pixel with a feature tensor by combining the high-dimensional feature information of all the pixels within its local neighborhood. The tensor representation mode integrates the polarimetric information and spatial information, which is beneficial for alleviating the influence of speckle noise. Then, the tensor discriminative locality alignment (TDLA) method is introduced to seek the multilinear transformation from the original polarimetricspatial feature tensor to the low-dimensional feature. The label information of training samples is utilized during feature transformation and feature mapping; therefore, the discriminability of different classes can be well preserved. Based on the extracted features in the low-dimensional space, the SVM classifier is applied to achieve the final classification result. The experiments implemented on two real PolSAR data sets verify that the proposed method can extract the features with better stability and separability, and obtain superior classification results compared to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar tomography (TomoSAR) is an extension of synthetic aperture radar (SAR) imaging. It introduces the synthetic aperture principle into the elevation direction to achieve three-dimensional (3-D) reconstruction of the observed target. Compressive sensing (CS) is a favorable technology for sparse elevation recovery. However, for the non-sparse elevation distribution of the forested areas, if CS is selected to reconstruct it, it is necessary to utilize some orthogonal bases to first represent the elevation reflectivity sparsely. The iterative adaptive approach (IAA) is a non-parametric algorithm that enables super-resolution reconstruction with minimal snapshots, eliminates the need for hyperparameter optimization, and requires fewer iterations. This paper introduces IAA to tomographicinversion of the forested areas and proposes a novel multi-polarimetric-channel joint 3-D imaging method. The proposed method relies on the characteristics of the consistent support of the elevation distribution of different polarimetric channels and uses the L 2 -norm to constrain the IAA-based 3-D reconstruction of each polarimetric channel. Compared with typical spectral estimation (SE)-based algorithms, the proposed method suppresses the elevation sidelobes and ambiguity and, hence, improves the quality of the recovered 3-D image. Compared with the wavelet-based CS algorithm, it reduces computational cost and avoids the influence of orthogonal basis selection. In addition, in comparison to the IAA, it demonstrates greater accuracy in identifying the support of the elevation distribution in forested areas. Experimental results based on BioSAR 2008 data are used to validate the proposed method. [ABSTRACT FROM AUTHOR]

Permafrost is a significant part of the cryosphere, which has gained increasing attention from scientists, policy-makers, and the general public due to global warming, environmental degradation, water shortages, and intense human activities. Although many permafrost research review articles have been published, these studies were predominantly limited to either one subject or one field, while systematic studies about permafrost based on bibliometric analysis methods remain limited. We aim to fill this gap by conducting a bibliometric analysis of 13,697 articles in the field of permafrost research from 1942 to 2021, collected from the Web of Science core collection database. The results indicate that permafrost research is a typically multi-author, multi-country, and multi-institution cooperative field, involved in many research fields. The cumulative number of publications has presented an exponential increase over the past 80 years, with an average annual growth rate of 10.40%. Since 2000, China has seen a rapid growth in the number of publications per year, surpassing the USA in 2016 and leading in the years since then. In addition, the authors from China have great contributions in publications, and there is good room for permafrost development in the future according to the authors' M-index ranking. After the analysis of authors' keywords, we found that, compared to the conventional methods, machine learning and interferometric synthetic aperture radar (InSAR) are new technological approaches introduced in recent years, and the Qinghai–Tibet Plateau has become a popular study area. The results presented here can help related researchers, scholars, and students in the field to better understand the past developments, current status, and future trends of permafrost research. Furthermore, this paper presents and expands the general process of the bibliometric method used in permafrost studies, which can provide researchers with new inspirations and improve discipline research approach. [ABSTRACT FROM AUTHOR]

Polarimetric calibration is one of the preprocessing steps in the quantitative processing of Polarimetric synthetic aperture radar (PolSAR) data, and its accuracy will affect subsequent applications. At present, the polarimetric calibration method based on distributed targets is widely used, and this kind of method needs to extract distributed targets that satisfy certain scattering characteristics as the calibration reference ground object samples before calibrating. Therefore, the extraction accuracy of distributed targets has a great influence on the accuracy of polarimetric calibration methods based on such targets. Therefore, this paper proposes a new distribution target extraction method, which is based on the idea of KS hypothesis testing, and uses the homogeneity of the pixels in the window to determine whether it is a distribution target. To verify the effectiveness of the method, the X-band airborne PolSAR images are used as the data of the polarimetric calibration experiment. Experiments show that, compared with other extraction methods, our method can not only ensure the extraction accuracy of distributed targets, but also further improve the accuracy of polarimetric calibration. [ABSTRACT FROM AUTHOR]

Image brightness compensation processing is one of the important aspects to decide whether the image can be further used for remote sensing quantitative applications. In this paper, a brightness compensation enhancement processing method based on nonsubsampled contourlet transform (NSCT) is used to address the widely used problems of synthetic aperture radar (SAR) images with unbalanced internal brightness and blurred detailed texture features and severe image SAR. Firstly, the image is decomposed using the NSCT transform and the sub-band coefficients are adjusted after calculating the improvement coefficients for the statistical radiation change curve of its low-frequency sub-bands to achieve brightness compensation; for each high-frequency sub-band image, a hard threshold function is used to enhance the contour texture information of the image and suppress noise; finally, all sub-bands are combined using NSCT inverse reconstruction to obtain the resultant image. Aiming at the problem of radiation discrepancies arising from SAR images due to its side-view observation method and antenna directional map, this method is more applicable to SAR images and has better improvement effects than the traditional image equalization algorithm, increasing the average gradient and structural similarity, better preserving the detail features, significantly improving the brightness non-uniformity problem, enhancing the visual effect and providing some support for the subsequent monitoring of various disasters. [ABSTRACT FROM AUTHOR]

The R. W. P. King Award is presented to an author under 36 years of age for the best paper published in the IEEE Transactions on Antennas and Propagation during the previous year. [ABSTRACT FROM AUTHOR]

Coherent change detection is a technology that utilizes phase information in complex-valued synthetic aperture radar images. It is mostly used to detect subtle changes that cannot be detected by amplitude images on the ground, and it also has excellent detection performance when it comes to low-coherence typical changed areas. However, due to its high sensitivity to changes, this technology will falsely detect areas of natural change such as vegetation disturbance, river flow, and low signal-to-noise ratio areas (e.g., uninteresting areas) as changes, resulting in false-alarm interference areas. In order to tackle this problem, this paper studies a coherent change detection method based on multi-scale analysis to extract typical changed areas in complicated scenes. The method uses an equal variance coherence estimator to calculate the coherence value, separates the interference areas and the typical changed areas using a multi-scale method, and then extracts a binary image of the typical changed areas through noise filtering and threshold segmentation. The method in this paper is experimentally verified with publicly available Airbus spaceborne SAR data, and ESAR airborne data, which is provided by the ESA. The experimental results are visualized and quantitatively evaluated. Through the results, by calculating the probability of correct classification and false-positive and other performance parameters, as well as drawing the receiver operating characteristic curve and the kappa coefficient curve of different threshold values, we find that the method has the capability to suppress the interference areas and the high detection performance of the typical changed areas. The experimental data are complicated scenes that include various types of ground object changes. The results show that the method is effective and universal and can provide reference value for the application of coherent change detection. [ABSTRACT FROM AUTHOR]

Arid zone grassland is a crucial component of terrestrial ecosystems and plays a significant role in ecosystem protection and soil erosion prevention. However, accurately mapping grassland spatial information in arid zones presents a great challenge. The accuracy of remote sensing grassland mapping in arid zones is affected by spectral variability caused by the highly diverse landscapes. In this study, we explored the potential of a rectangular tile classification model, constructed using the random forest algorithm and integrated images from Sentinel-1A (synthetic aperture radar imagery) and Sentinel-2 (optical imagery), to enhance the accuracy of grassland mapping in the semiarid to arid regions of Ordos, China. Monthly Sentinel-1A median value images were synthesised, and four MODIS vegetation index mean value curves (NDVI, MSAVI, NDWI and NDBI) were used to determine the optimal synthesis time window for Sentinel-2 images. Seven experimental groups, including 14 experimental schemes based on the rectangular tile classification model and the traditional global classification model, were designed. By applying the rectangular tile classification model and Sentinel-integrated images, we successfully identified and extracted grasslands. The results showed the integration of vegetation index features and texture features improved the accuracy of grassland mapping. The overall accuracy of the Sentinel-integrated images from EXP7-2 was 88.23%, which was higher than the accuracy of the single sensor Sentinel-1A (53.52%) in EXP2-2 and Sentinel-2 (86.53%) in EXP5-2. In all seven experimental groups, the rectangular tile classification model was found to improve overall accuracy (OA) by 1.20% to 13.99% compared to the traditional global classification model. This paper presents novel perspectives and guidance for improving the accuracy of remote sensing mapping for land cover classification in arid zones with highly diverse landscapes. The study presents a flexible and scalable model within the Google Earth Engine framework, which can be readily customized and implemented in various geographical locations and time periods. [ABSTRACT FROM AUTHOR]

Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm's efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining its computational efficiency. However, the above method is only operated in the azimuth direction, and the computing efficiency needs to be urgently improved in the actual processing process. This paper proposes a fast factorized back-projection algorithm for stripmap SAR imaging based on range block division. The echo data are divided into multiple subblocks in the range direction, and FFBP processing is applied separately to each full-aperture subblock, further enhancing computational efficiency. The paper analyzes the algorithm's principles, underscores the necessity of integral aperture determination and full-aperture data block processing, provides specific implementation steps, and applies the algorithm to point target simulation and experimental data from a vehicle-mounted ice radar. The experiments validate the algorithm's efficiency in stripmap SAR imaging. [ABSTRACT FROM AUTHOR]

Due to the excellent characteristics of Synthetic Aperture Radar (SAR), it has become an important technical means for land and sea target detection applications worldwide. However, the SAR image imaging process is often accompanied by speckle noise, which affects the accuracy of target detection. For this problem, this paper proposes an SAR image reconstruction model based on image denoising and feature extraction. Given a noisy SAR image, at each pixel of the image, the value of the singularity index of a metric defined by the signal gradient norm density is calculated. Then through a hierarchical decomposition of these singular indices, a set of pixels containing the most informative feature set of the image is extracted, and a denoised image is reconstructed using the extracted features. In comparison to traditional filtering and denoising techniques such as non-local filtering, this algorithm achieves remarkable local filtering and denoising effects while preserving the image's texture information. The denoising performance of the proposed method is evaluated through a series of quantitative indicators and visual comparisons. The results demonstrate that this method effectively reduces background clutter, resulting in a higher contrast between the target and the background. [ABSTRACT FROM AUTHOR]

Dedicated to synchronously acquiring large-area, high-precision, and multi-scale ocean wind and wave information, a novel concept of a spaceborne ocean microwave dual-function integrated sensor is proposed in this paper. It integrates the functions of a scatterometer and SAR by sharing a single phased-array antenna. An overview of the scientific requirements and motivations for the sensor are outlined firstly. In order to fulfill the observation requirements of both the functions, the constraints on the system parameters such as frequency, antenna size, and incidence angle are analyzed. Then, the selection principles of these parameters are discussed within the limitations of antenna area, bandwidth, available time, and cost. Additionally, the constraints on the time sequence of transmitting and receiving pulses are derived to ensure that there is no conflict when the two functions operate simultaneously. Subsequently, a method for jointly designing the pulse repetition frequency (PRF) of both the functions is introduced, along with zebra maps to verify its effectiveness. At the end of the paper, the system and performance parameters of the sensor are given for further insight into it. [ABSTRACT FROM AUTHOR]

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SARs) and Ground-Penetrating Radars (GPRs) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency-Modulated Continuous Wave (FMCW) operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device. In one scenario, it was able to detect and locate a 15 cm-diameter fire-extinguisher located at a distance of 3.5 m from the scanning system and 1.6 m behind a 3 cm-thick MDF (medium-density fiberboard) wall with cm-level accuracy. In a second test, the proposed system was used to perform interferometric displacement measurements, and it was capable of determining the displacement of a metal case with sub-millimeter accuracy. In a third experiment, the system was used to construct a 3D image of the inside of a wood table with cm-level resolution. [ABSTRACT FROM AUTHOR]

The stability of loess landslides affects the production and livelihood of the people in its vicinity. The stability of loess landslides is influenced by various factors, including internal structure, collapsibility, water content, and shear strength. The landslide stability of loesses can be analyzed by several geophysical methods, such as seismic refraction tomography (SRT), electrical resistivity tomography (ERT), micro-seismic technology, and ground penetrating radar (GPR). Geotechnical tests (compression and shear tests) and remote sensing techniques (Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR) and airborne 3D laser technology) are used for studying the landslide stability of loesses as well. Some of the methods above can measure parameters (e.g., fractures, water content, shear strength, creep) which influence the stability of loess landslides, while other methods qualitatively indicate the influencing factors. Integrating parameters measured by different methods, minimizing disturbances to landslides, and assessing landslide stability are important steps in studying landslide hazards. This paper comprehensively introduces the methods used in recent studies on the landslide stability of loesses and summarizes the factors which affect the landslide stability. Furthermore, the relationships between different parameters and methods are examined. This paper enhances comprehension of the underlying mechanisms of the stability of loess landslides to diminish disastrous consequences. [ABSTRACT FROM AUTHOR]

This paper illustrates an interferometric inverse synthetic aperture radar based on three‐dimensional imaging and rotational velocity estimation from a multistatic setup with three ground‐based radars with non‐orthogonal baselines. The radar backscattering along the orbit is simulated based on physical optics approximation. The paper illustrates a novel shape‐dependent and rotation‐invariant feature called normalised eigenvalues for classification. A database of interferometric point clouds is generated using radar back scattering at different aspect angles and classified using various classification algorithms. The classification accuracy at varying SNR levels was evaluated using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Due to the strong penetrability, long-wavelength synthetic aperture radar (SAR) can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media. SAR tomography (TomoSAR) technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights, providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects. As an emerging technology, it is different from the traditional interferometric SAR (InSAR) technology and has advantages in reconstructing the three-dimensional structure of the illuminated media. Over the past two decades, many TomoSAR methods have been proposed to improve the vertical resolution, aiming to distinguish the locations of different scatters in the unit pixel. In order to cope with the forest mission of European Space Agency (ESA) that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests, it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology. In this paper, we adopt three typical algorithms, namely, Capon, Multiple Signal Classification (MUSIC), and Compressed Sensing (CS), to evaluate the performance in forest height and underlying topography inversion. The P-band airborne full-polarization (FP) SAR data of Lopè National Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment. Furthermore, we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile. The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions. Compared with LiDAR results, the root-mean-square error (RMSE) of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m, which performs the best among the three algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a method for quantifying the three-dimensional deformation of ground targets and outlines the associated process. Initially, ground-based synthetic aperture radar was employed to monitor the radial deformation of targets, and optical equipment monitored pixel-level deformation in the vertical plane of the line of sight. Subsequently, a regression model was established to transform pixel-level deformation into two-dimensional deformation based on a fundamental length unit, and the radar deformation monitoring data were merged with the optical deformation monitoring data. Finally, the fused data underwent deformation, resulting in a comprehensive three-dimensional deformation profile of the target. Through physical data acquisition experiments, the comprehensive three-dimensional deformation of targets was obtained and compared with the actual deformations. The experimental results show that the method has a relative error of less than 10%, and monitoring accuracy is achieved at the millimeter level. [ABSTRACT FROM AUTHOR]

As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model's lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks. [ABSTRACT FROM AUTHOR]

Nearshore ship detection has significant applications in both the military and civilian domains. Compared to synthetic aperture radar (SAR), polarimetric synthetic aperture radar (PolSAR) provides richer information for analyzing the scattering mechanisms of ships and enables better detection of ship targets. However, ships in nearshore areas tend to be highly concentrated, and ship detection is often affected by adjacent strong scattering, resulting in false alarms or missed detections. While the GP-PNF detector performs well in PolSAR ship detection, it cannot obtain satisfactory results in these scenarios, and it also struggles in the presence of azimuthal ambiguity or strong clutter interference. To address these challenges, we propose a nearshore ship detection method named ECD-PNF by integrating superpixel-level GP-PNF and refined polarimetric decomposition. Firstly, polarimetric superpixel segmentation and sea–land segmentation are performed to reduce the influence of land on ship detection. To estimate the sea clutter more accurately, an automatic censoring (AC) mechanism combined with superpixels is used to select the sea clutter superpixels. By utilizing refined eight-component polarimetric decomposition to improve the scattering vector, the physical interpretability of the detector is enhanced. Additionally, the expression of polarimetric coherence is improved to enhance the target clutter ratio (TCR). Finally, this paper combines the third eigenvalue of eigenvalue–eigenvector decomposition to reduce the impact of azimuthal ambiguity. Three spaceborne PolSAR datasets from Radarsat-2 and GF-3 are adopted in the experiments for comparison. The proposed ECD-PNF method achieves the highest figure of merit (FoM) value of 0.980, 1.000, and 1.000 for three datasets, validating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) is a high-resolution imaging sensor commonly mounted on platforms such as airplanes and satellites for widespread use. In complex electromagnetic environments, radio frequency interference (RFI) severely degrades the quality of SAR images due to its widely varying bandwidth and numerous unknown emission sources. Although traditional deep learning-based methods have achieved remarkable results by directly processing SAR images as visual ones, there is still considerable room for improvement in their performance due to the wide coverage and high intensity of RFI. To address these issues, this paper proposes the fusion of segmentation and inpainting networks (FuSINet) to suppress SAR RFI in the time-frequency domain. Firstly, to weaken the dominance of RFI in SAR images caused by high-intensity interference, a simple CCN-based network is employed to learn and segment the RFI. This results in the removal of most of the original interference, leaving blanks that allow the targets to regain dominance in the overall image. Secondly, considering the wide coverage characteristic of RFI, a U-former network with global information capture capabilities is utilized to learn the content covered by the interference and fill in the blanks created by the segmentation network. Compared to the traditional Transformer, this paper enhances its global information capture capabilities through shift-windows and down-sampling layers. Finally, the segmentation and inpainting networks are fused together through a weighted parameter for joint training. This not only accelerates the learning speed but also enables better coordination between the two networks, leading to improved RFI suppression performance. Extensive experimental results demonstrate the substantial performance enhancement of the proposed FuSINet. Compared to the PISNet+, the proposed attention mechanism achieves a 2.49 dB improvement in peak signal-to-noise ratio (PSNR). Furthermore, compared to Uformer, the FuSINet achieves an additional 4.16 dB improvement in PSNR. [ABSTRACT FROM AUTHOR]

This paper presents a method for extracting the digital elevation model (DEM) of forested areas from polarimetric interferometric synthetic aperture radar (PolInSAR) data. The method models the ground phase as a Von Mises distribution, with a mean of the topographic phase computed from an external DEM. By combining the prior distribution of the ground phase with the complex Wishart distribution of the observation covariance matrix, we derive the maximum a posterior (MAP) inversion method based on the RVoG model and analyze its Cramer–Rao Lower Bound (CRLB). Furthermore, considering the characteristics of the objective function, this paper introduces a Four-Step Optimization (FSO) method based on gradient optimization, which solves the inefficiency problem caused by exhaustive search in solving ground phase using the MAP method. The method is validated using spaceborne L-band repeat-pass SAOCOM data from a test forest area. The test results for FSO indicate that it is approximately 5.6 times faster than traditional methods without compromising accuracy. Simultaneously, the experimental results demonstrate that the method effectively solves the problem of elevation jumps in DEM inversion when modeling the ground phase with the Gaussian distribution. ICESAT-2 data are used to evaluate the accuracy of the inverted DEM, revealing that our method improves the root mean square error (RMSE) by about 23.6% compared to the traditional methods. [ABSTRACT FROM AUTHOR]

This paper presents a forward modeling method for the scattering center (SC) model of dynamic ships on time-varying sea surfaces, tailored for remote sensing and target-recognition applications. Grounded in ship hydrodynamics, the methodology delineates ship movements amidst fluctuating waves, harnessing computer graphics to integrate ship–sea geometries across diverse temporal instances. Utilizing the four-path model, the composite scattering effects are segregated into distinct ship and sea contributions, along with their mutual interactions. Augmented by high-frequency electromagnetic principles, the paper quantifies and deduces SC parameters, culminating in a 3-D parameterized SC model for complex maritime targets. Unlike conventional inverse methods, this approach employs a "cause-to-effect" forward strategy, establishing clear links between SCs and local geometries, enhancing the model's physical clarity. Using the fishing ship as a case, this research compared the normalized similarity index and position-matching rate between the reconstructed synthetic aperture radar (SAR) image and the simulated SAR image. The results indicate that all computed results exceeded 90%. Furthermore, a comparison was conducted between the reconstructed radar cross-sections (RCS) obtained by expanding the model within a large angular range and the simulated results. The root mean square error between the two was less than 3 dB, affirming the accuracy and effectiveness of the proposed model. Additionally, the research examines the variations in SCs during the six-degrees-of-freedom motions, providing a detailed quantitative analysis of their temporal trends in amplitude and position. In summary, this investigation furnishes an efficient and economical framework for rapid radar characterization in dynamic, variable marine environments, fostering advancements in remote sensing and maritime target identification. [ABSTRACT FROM AUTHOR]

Polar format algorithm (PFA) is a widely used high-resolution SAR imaging algorithm that can be implemented in advanced widefield staring synthetic aperture radar (WFS-SAR). However, existing algorithms have limited analysis in wavefront curvature error (WCE) and are challenging to apply to WFS-SAR with high-resolution and large-swath scenes. This paper proposes an extended polar format algorithm for joint envelope and phase error correction in WFS-SAR imaging with maneuvering trajectory. The impact of the WCE and residual acceleration error (RAE) are analyzed in detail by deriving the specific wavenumber domain signal based on the mapping relationship between the geometry space and wavenumber space. Subsequently, this paper improves the traditional WCE compensation function and introduces a new range cell migration (RCM) recalibration function for joint envelope and phase error correction. The 2D precisely focused SAR image is acquired based on the spatially variant inverse filtering in the final. Simulation experiments validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, successful in computer vision and optical satellite image matching, faces challenges when applied to satellite SAR images due to the presence of speckle noise, leading to increased matching errors. The SAR–SIFT method is explored and analyzed in-depth, considering the unique characteristics of satellite SAR images. To enhance the efficiency of matching identical feature points in two satellite SAR images, the paper proposes a Graphics Processing Unit (GPU) mapping implementation based on the SAR–SIFT algorithm. The paper introduces a multi-GPU collaborative acceleration strategy for SAR image matching. This strategy addresses the challenge of matching feature points in the region and embedding multiple SAR images in large areas. The goal is to achieve efficient matching processing of multiple SAR images in extensive geographical regions. The proposed multi-GPU collaborative acceleration algorithm is validated through experiments involving feature point extraction and matching using 21 GF-3 SAR images. The results demonstrate the feasibility and efficiency of the algorithm in enhancing the processing speed of matching feature points in large-scale satellite SAR images. Overall, the paper contributes to the advancement of SAR image processing techniques, specifically in feature point extraction and matching in large-scale applications. [ABSTRACT FROM AUTHOR]

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were −90.78 mm and −83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are −35.38 mm/y and −30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning. [ABSTRACT FROM AUTHOR]