Search

Spaceborne synthetic aperture radar (SAR) is an advanced microwave imaging technology that provides all-weather and all-day target information. However, as spaceborne SAR resolution improves, traditional echo signal models based on airborne SAR design become inadequate due to the curved orbit, Earth rotation, and increased propagation distance. In this study, we propose an accurate range model for high-resolution spaceborne SAR by analyzing motion trajectory and Doppler parameters from the perspective of the space geometry of spaceborne SAR. We evaluate the accuracy of existing range models and propose an advanced equivalent squint range model (AESRM) that accurately fits the actual range history and compensates for high-order term errors by introducing third-order and fourth-order error terms while maintaining the simplicity of the traditional model. The proposed AESRM's concise two-dimensional frequency spectrum form facilitates the design of imaging algorithms. Point target simulations confirm the effectiveness of the proposed AESRM, demonstrating significant improvements in fitting accuracy for range histories characterized by nonlinear trajectories. The developed AESRM provides a robust foundation for designing imaging algorithms and enables higher resolution and more accurate radar imaging. [ABSTRACT FROM AUTHOR]

Multistatic synthetic aperture radar (SAR) is a special mode of SAR system. The radar transmitter and receiver are located on different satellites, which brings many advantages, such as flexible baseline configuration, diverse receiving modes, and more detailed ground object classification information. The multistatic SAR has been widely used in interferometry, moving target detection, three-dimensional imaging, and other fields. The frequency offset between different oscillators will cause a modulation phase error in the signal. Therefore, phase synchronization is one of the most critical problems to be addressed in distributed SAR systems. This article reviews phase synchronization techniques, which are mainly divided into two methods: synchronization by direct microwave link and synchronization by a data-based estimation algorithm. Furthermore, the future development of synchronization technology is anticipated. [ABSTRACT FROM AUTHOR]

LuTan-1 (LT-1) is a bistatic synthetic aperture radar (BiSAR) system consisting of two identical L-band SAR satellites. The bistatic mode of LT-1 plays a critical role in generating high-precision digital elevation models (DEMs), which requires precise geometric calibration of initial range and azimuth times for both SARs to ensure the reliability and quality of geolocation. However, existing geometric calibration methods predominantly focus on monostatic SAR systems, with limited literature on slave SAR calibration in bistatic systems. This research addresses this gap by establishing geometric calibration models for both SARs based on signal echo history and the range–Doppler model. The geometric errors are effectively resolved using corner reflector data from Xinjiang, China. Through statistical analysis of LT-1 SAR images acquired between July and November in bistatic mode, this paper has demonstrated range delay accuracy of better than 5 ns and azimuth time accuracy of better than 0.1 ms. This level of precision translates into a positional accuracy better than 0.8 m. The proposed models have been successfully applied to geometric calibration, providing precise geolocation for LT-1, thus enhancing its utility for a wide range of Earth observation applications. This paper is the first endeavor to present the assessment of the geometric calibration and geolocation accuracy of LT-1 and discuss the results of the bistatic geometric calibration of the master and slave SARs in a BiSAR formation. [ABSTRACT FROM AUTHOR]

River water surface extent can be extracted from optical and radar satellite images; this is useful for estimating streamflow from space. The radiation characteristics of open water from the visible and microwave bands are different and provide independent information. In this study, for the purpose of improving streamflow estimation from space for data-sparse regions, a method that combines satellite optical and radar images data for streamflow estimation using a machine learning technique was proposed. The method was demonstratedthrough a case study in the river segment upstream of the Ganzi gauging station on the Yalong River, China. Utilizing the support vector regression (SVR) model, the feasibility of different combinations of water surface area derived from Sentinel-1 synthetic aperture radar images (AREA_SAR), modified normalized difference water index derived from Landsat 8 images (MNDWI), and reflectance ratios between NIR and SWIR channels derived from MODIS images (RNIR/RSWIR) for streamflow estimation were evaluated through three experiments. In Experiment I, three models using AREA_SAR (Model 1), MNDWI (Model 2), and a combination of AREA_SAR and MNDWI (Model 3) were built; the mean relative error (MRE) and mean absolute error (MAE) of streamflow estimates corresponding to the SVR model using both AREA_SAR and MNDWI (Model 3) were 0.19 and 31.6 m3/s for the testing dataset, respectively, and were lower than two models using AREA_SAR (Model 1) or MNDWI (Model 2) solely as inputs. In Experiment II, three models with AREA_SAR (Model 4), RNIR/RSWIR (Model 5), and a combination of AREA_SAR and RNIR/RSWIR (Model 6) as inputs were developed; the MRE and MAE for the model using AREA_SAR and RNIR/RSWIR (Model 6) were 0.25 and 56.5 m3/s, respectively, which outperformed the two models treating AREA_SAR (Model 4) or MNDWI (Model 5) as single types of inputs. In Experiment III, three models using AREA_SAR (Model 7), MNDWI, and RNIR/RSWIR (Model 8) and the combination of AREA_SAR, MNDWI and RNIR/RSWIR (Model 9) were built; combining all three types of satellite observations (Model 9) exhibited the highest accuracy, for which the MRE and MAE were 0.18 and 18.4 m3/s, respectively. The results of all three experiments demonstrated that integrating optical and microwave observations could improve the accuracy of streamflow estimates using a data-driven model; the proposed method has great potential for near-real-time estimations of flood magnitude or to reconstruct past variations in streamflow using historical satellite images in data-sparse regions. [ABSTRACT FROM AUTHOR]

Conventional squinted sliding spotlight synthetic aperture radar (SAR) imaging suffers from substantial swath width reduction and complex processing requirements due to the continuous variation in the squint angle and the large range cell migration (RCM) throughout the data acquisition interval. A novel two-dimensional (2D) beam scanning mode for high-resolution wide swath (HRWS) imaging is proposed. The key to the novel imaging mode lies in the synchronous scanning of azimuth and range beams, allowing for a broader and more flexible imaging swath with a high geometric resolution. Azimuth beam scanning from fore to aft was used to improve the azimuth resolution, while range beam scanning was adopted to illuminate the oblique wide swath to avoid the large RCM and the serious swath width reduction. Compared with the conventional sliding spotlight mode, both the swath width and swath length could be extended. According to the echo model of this imaging mode, an echo signal preprocessing approach is proposed. The key points of this approach are range data extension and azimuth data upsampling. A designed system example with a resolution of 0.5 m, swath width of 60 km, and azimuth coverage length of 134 km is presented. Furthermore, a simulation experiment on point targets was carried out. Both the presented system example and imaging results of point targets validated the proposed imaging mode. [ABSTRACT FROM AUTHOR]

Bogotá, the largest urban center and capital city of Colombia, is located within the Bogotá savanna, which originated as a lake in the central part of the Colombian Eastern Cordillera. Over time, the lake transformed into a gently undulating plain with horizontally deposited sediments that formed around five million years ago. Over the last few decades, the region has undergone significant population growth and rapid urban development, largely driven by migration from rural areas. This development has substantially impacted the subsidence observed in the city, primarily due to the extraction of groundwater. A previous study by the Servicio Geológico Colombiano (SGC) utilized data from GNSS stations and synthetic aperture radar interferometry (InSAR) with TerraSAR-X SAR between 2011 and 2017 to identify a subsidence pattern in the central region of Bogotá. The purpose of the study was to evaluate the risks and potential disasters associated with the subsidence phenomenon. Our study investigates both the subsidence in Bogotá, previously studied, as well as the rural savanna area, which is currently undergoing significant residential and industrial development. We utilized multi-temporal InSAR (MT-InSAR) techniques with Sentinel-1 SAR images from 2014 to 2021. The analysis results indicate that the outer regions of the city display the most significant subsidence, extending from the center to the north. The subsidence velocities in these areas reach approximately 5–6 cm/year. [ABSTRACT FROM AUTHOR]

For a single-channel spaceborne synthetic aperture radar (SAR), the usage of a low pulse repetition frequency (PRF) is an effective technical way to extend the range swath. The sub-aperture imaging strategy is usually used to solve the problem of azimuth spectrum aliasing under the condition of a low PRF. However, the required up-sampling processing before the coherent synthesis of sub-images will lead to spectrum discontinuity between adjacent sub-images, which leads to an obvious grating lobe phenomenon after the process of sub-image synthesis, resulting in a significant decrease in image quality. For this issue, a high-resolution wide-swath (HRWS) imaging algorithm for a spaceborne SAR with a low PRF is proposed in this paper based on optimal spectrum shift processing. First, each sub-aperture is imaged using the typical range migration algorithm (RMA), and then all sub-images are up-sampled at the same time. Then, based on the criterion of the minimum grating lobe, the optimal spectrum shift is estimated. Finally, the spectrum of all sub-images is shifted and then all the shifted sub-images are synthesized coherently. The simulation data processing results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In 1978, the SEASAT satellite was launched, carrying the first civilian synthetic aperture radar (SAR). The mission was the monitoring of ocean: application to land was also studied. Despite its short operational time of 105 days, SEASAT-SAR provided a wealth of information on land and sea, and initiated many spaceborne SAR programs using not only the image intensity data, but also new technologies of interferometric SAR (InSAR) and polarimetric SAR (PolSAR). In recent years, artificial intelligence (AI), such as deep learning, has also attracted much attention. In the present article, a review is given on the imaging processes and analyses of oceanic data using SAR, InSAR, PolSAR data and AI. The selected oceanic phenomena described here include ocean waves, internal waves, oil slicks, currents, bathymetry, ship detection and classification, wind, aquaculture, and sea ice. [ABSTRACT FROM AUTHOR]

This paper presents the design and processing of the SAR acquisition technique named frequency scanning (f-SCAN), aimed to obtain high sensitivity to targets with low backscattering and to improve the signal-to-noise ratio (SNR) in wide-swath systems. The f-SCAN is an interesting alternative to the scanning on receive method (SCORE), which needs multiple phase centres achieved using the digital beam forming (DBF) technique. f-SCAN requires less hardware complexity than SCORE; at the same time, it improves the sidelobes and ambiguities' suppression. The elements used in f-SCAN to generate the pencil beam are the true time delay lines (TTDLs) and the phase shifters (PSs). The general methodology to design an f-SCAN spaceborne SAR high-resolution wide-swath (HRWS) system is introduced; emphasis is put on the mathematical definition of the timing parameters and on a novel method of using TTDLs to achieve the full spanning of wide swaths. The processing of f-SCAN data is also considered: we introduce a novel algorithm to limit the data volume and to guarantee an almost invariant slant range impulse response function (IRF) by removing spectral distortions. Eventually, new definitions, specific for f-SCAN, of the well-known SAR performance parameters, are provided. Simulation results and performances are presented. The advantages and disadvantages with respect to SCORE are discussed using the design of a real case system. [ABSTRACT FROM AUTHOR]

This study focuses on a very complex environment, namely the Ionian coast of the Basilicata region, Southern Italy, which includes different kinds of beaches, river mouths and built-up areas. This complex environment is used as a test case to analyze the time variability of the coastline using measurements that were remotely sensed by the satellite European Copernicus Synthetic Aperture Radar (SAR) mission. First, the accuracy of the coastline, extracted by the SAR, is discussed with respect to finer-spatial-resolution drone-based light detection and ranging (LIDAR) measurements. Then, a time series of SAR dual-polarimetric measurements acquired by the European Copernicus mission is used to discuss the time variability of the coastline of the area of interest in a time period spanning from 2015 to 2021. The experimental results show that the accuracy of the SAR-based coastline is better than 15 m, which is reasonably good precision for monitoring the erosion/accretion processes that characterize the area of interest at a moderate scale. The estimated time variability of the extracted coastline suggests a dominant erosion process, which is always within 60 m. [ABSTRACT FROM AUTHOR]

Owing to the Moon's rough surface, there is a growing controversy over the conclusion that water ice exists in the lunar permanently shadowed regions (PSRs) with a high circular polarization ratio (CPR). To further detect water ice on the Moon, an innovative design method for spaceborne synthetic aperture radar (SAR) system is proposed, to obtain radar data that can be used to distinguish water ice from lunar regolith with a small difference in the dielectric constants. According to Campbell's dielectric constant model and the requirement that SAR radiometric resolution is smaller than the contrast of targets in images, a newly defined SAR system function involved in the method is presented to evaluate the influence of some system parameters on the water ice detection capability of SAR. In addition, several simulation experiments are performed, and the results demonstrate that the presented SAR design method may be helpful for lunar water ice exploration. [ABSTRACT FROM AUTHOR]

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposed an attention mechanism for multi-scale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), was proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our method is more lightweight, only 18.43 M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy ( A P 50 ) of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 2.6% and 3.9% higher than that of YOLOv7, respectively. [ABSTRACT FROM AUTHOR]

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Ground-based synthetic aperture radar (GBSAR) has the advantage of retrieving submillimeter deformation of the mine slope by using the differential interferometry technique, which is important for safe production in mining applications. However, the moving vehicle's defocus/displaced signal will mask the SAR image of the mining area which affects the accuracy of interference phase extraction and deformation inversion. In order to remove its influence, the moving target can first be refocused and then removed. To our knowledge, there is no GBSAR moving target refocusing method currently. Hence, the refocusing method is necessary. To solve the above problem, this paper proposes a single-channel FMCW-GBSAR moving target refocusing method based on relative speed. Firstly, the FMCW-GBSAR moving target signal model is analyzed, and then the relative speed based signal model is deduced. Based on the model and GBSAR's feature of incomplete synthetic aperture, the Range Doppler (RD) algorithm is adopted and improved to achieve refocusing using relative speed parameters. The algorithm is controlled by relative speed and squint angle; thus, the refocused target image can be obtained via searching 2D parameters. The proposed method is verified by the synthetic data, which are generated by combining NCUT FMCW GBSAR real data and simulated moving target echo. [ABSTRACT FROM AUTHOR]

The detection of ships on the open sea is an important issue for both military and civilian fields. As an active microwave imaging sensor, synthetic aperture radar (SAR) is a useful device in marine supervision. To extract small and weak ships precisely in the marine areas, polarimetric synthetic aperture radar (PolSAR) data have been used more and more widely. We propose a new PolSAR ship detection method which is based on a keypoint detector, referred to as a PolSAR-SIFT keypoint detector, and a patch variation indicator in this paper. The PolSAR-SIFT keypoint detector proposed in this paper is inspired by the SAR-SIFT keypoint detector. We improve the gradient definition in the SAR-SIFT keypoint detector to adapt to the properties of PolSAR data by defining a new gradient based on the distance measurement of polarimetric covariance matrices. We present the application of PolSAR-SIFT keypoint detector to the detection of ship targets in PolSAR data by combining the PolSAR-SIFT keypoint detector with the patch variation indicator we proposed before. The keypoints extracted by the PolSAR-SIFT keypoint detector are usually located in regions with corner structures, which are likely to be ship regions. Then, the patch variation indicator is used to characterize the context information of the extracted keypoints, and the keypoints located on the sea area are filtered out by setting a constant false alarm rate threshold for the patch variation indicator. Finally, a patch centered on each filtered keypoint is selected. Then, the detection statistics in the patch are calculated. The detection statistics are binarized according to the local threshold set by the detection statistic value of the keypoint to complete the ship detection. Experiments on three data sets obtained from the RADARSAT-2 and AIRSAR quad-polarization data demonstrate that the proposed detector is effective for ship detection. [ABSTRACT FROM AUTHOR]

The deployable reflector antenna based on the synthetic aperture radar is a satellite component that consists of a unit structure in the form of a folded reflector. During the launch process, this satellite antenna is in the stowed condition to improve storage efficiency. It is then deployed to perform the space mission in the on-orbit condition. Due to these structural characteristics of the deployable reflector antenna, the reflector is possible to be loaded in the limited volume of the launch vehicle with the reduced size. Additionally, because the deployable reflector antenna is made by the lightweight material of the carbon fiber reinforced polymer and honeycomb core, it can reduce the launching cost and improve the revisit interval. In this paper, the conceptual design of the main reflector of the deployable reflector antenna was conducted. The main reflector was designed as the honeycomb sandwich composite structure. To design the main reflector, the stacking sequence of the composite material and honeycomb core was investigated to maximize the structural stiffness and minimize the antenna's mass. Subsequently, finite element analyses including modal, quasi-static, structural–thermal coupling, and transient response were performed to numerically evaluate the structural performance of the lightweight composite reflector antenna. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks (CNNs) have shown significant advantages in the tasks of image classification; however, these usually require a large number of labeled samples for training. In practice, it is difficult and costly to obtain sufficient labeled samples of polarimetric synthetic aperture radar (PolSAR) images. To address this problem, we propose a novel semi-supervised classification method for PolSAR images in this paper, using the co-training of CNN and a support vector machine (SVM). In our co-training method, an eight-layer CNN with residual network (ResNet) architecture is designed as the primary classifier, and an SVM is used as the auxiliary classifier. In particular, the SVM is used to enhance the performance of our algorithm in the case of limited labeled samples. In our method, more and more pseudo-labeled samples are iteratively yielded for training through a two-stage co-training of CNN and SVM, which gradually improves the performance of the two classifiers. The trained CNN is employed as the final classifier due to its strong classification capability with enough samples. We carried out experiments on two C-band airborne PolSAR images acquired by the AIRSAR systems and an L-band spaceborne PolSAR image acquired by the GaoFen-3 system. The experimental results demonstrate that the proposed method can effectively integrate the complementary advantages of SVM and CNN, providing overall classification accuracy of more than 97%, 96% and 93% with limited labeled samples (10 samples per class) for the above three images, respectively, which is superior to the state-of-the-art semi-supervised methods for PolSAR image classification. [ABSTRACT FROM AUTHOR]

Near-field high-resolution synthetic aperture radar (SAR) imaging is mostly accompanied by a large number of data acquisition processes, which increases the system complexity and device cost. According to extensive reports, reducing the number of sampling points of a radar in space can greatly reduce the amount of data. However, when spatial sparse sampling is carried out, a ghost normally appears in the imaging results due to the high side lobes generated in the azimuth. To address this issue, a technique is introduced in this paper to recover the blank data through amplitude and phase compensation based on the correlation between sparse array sampling through adjacent points. Firstly, the data sampled by the sparse array is compressed in the range direction to obtain the expected data slices in the same range direction. Then, the blank element of the slice is compensated for with amplitude and phase to obtain full aperture data. Finally, the matched filter method is used to aid in the image reconstruction. The simulation results verified that the method proposed in this paper can effectively reconstruct the image under two kinds of sparse sampling conditions. Thus, a simple single-input single-output (SISO) synthetic aperture radar imaging test bench is established. Compared with the results of a 1 mm (1/4 λ) sampling interval, the quality of the reconstructed image under the condition of a 4 mm (1 λ) sampling interval still stands using our proposed method. Demonstrated by the experiment, the normalized root-mean-square error(NMSE) is 5.75%. Additionally, when the spatial sampling points are sampled randomly with 30% of the full sampling condition, this method can also restore and reconstruct the image with high quality. Due to the decrease of sampling points, the data volume can be reduced, which is beneficial for improving the scanning speed and alleviating the pressure of data transmission for near-field high resolution SAR imaging systems. [ABSTRACT FROM AUTHOR]

The azimuth multichannel synthetic aperture radar (MC-SAR) is an effective means of earth observation due to its high-resolution and wide-swath (HRWS) imaging capability. Compared with the traditional MC-SAR, the two-dimensional (2D) spectrum of the squint multichannel synthetic aperture radar (SMC-SAR) is skewed, leading to the severe coupling between azimuth signals and range signals. Therefore, the traditional imaging algorithms are inapplicable to SMC-SAR. Currently, the existing imaging algorithms for SMC-SAR need interpolation or sub-aperture processing, which results in low efficiency. This paper proposes a novel imaging scheme for spaceborne SMC-SAR to handle this issue. With the squint angle known, the Doppler centroid can be compensated for by the spectrum shift function. Additionally, the frequency correlation method (FCM) is utilized to estimate the channel phase imbalance. After that, the azimuth multichannel data are reconstructed by the filter banks to eliminate the Doppler spectrum aliasing. Finally, the data are imaged based on the modified equivalent slant range model (MESRM). Both the simulation and Gaofen-3 (GF-3) data experiments demonstrate the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) imagery is affected by the ionosphere, resulting in distortions of the SAR intensity, phase, and polarization. Although several methods have been proposed to mitigate the ionospheric phase delay of SAR interferometry, the application of them with full-polarimetric SAR interferometry is limited. Based on this background, Faraday rotation (FR)-based methods are used in this study to mitigate the ionospheric phase errors on full-polarimetric SAR interferometry. For a performance test of the selected method, L-band Advanced Land Observation Satellite (ALOS) Phase Array L-band SAR (PALSAR) full-polarimetric SAR images over high-latitude and low-latitude regions are processed. The result shows that most long-wavelength ionospheric phase errors are removed from the original phase after using the FR-based method, where standard deviations of the corrected result have decreased by almost a factor of eight times for the high-latitude region and 28 times for low-latitude region, compared to those of the original phase, demonstrating the efficiency of the method. This result proves that the FR-based method not only can mitigate the ionospheric effect on SAR interferometry, but also can map the high-spatial-resolution vertical total electronic content (VTEC) distribution. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) data provide an alternative way for topography retrieval, especially when limited PolSAR data are available. This article proposes a novel topography retrieval algorithm based on the Lambertian backscatter model that further improves the vertical precision of digital elevation model (DEM) generation and requires only one flight. The key idea of the proposed algorithm is to avoid data fluctuations caused by the ratio of the azimuth slope angle to the polarimetric orientation angle (POA). The previous research has confirmed the feasibility of generating a DEM based on single-pass PolSAR data, but its effect on the quality of reference DEM has not been well-explained. To analyze this effect, a large number of experiments on DEM with different resolutions are conducted. In addition, an in-depth analysis of non-linear and terrain-dependent errors is performed. The L-band PolSAR data of NASA/JPL TOPSAR and ALOS-2 PALSAR-2 and interferometric SAR (InSAR) DEM data are used to verify the proposed algorithm. The experimental results show that PolSAR data can be used as an additional reliable information source for DEM fusion under certain conditions to improve the quality of public DEM. [ABSTRACT FROM AUTHOR]

In the past two decades, ground-based synthetic aperture radars (GB-SARs) have developed rapidly, providing a large amount of SAR data in minutes or even seconds. However, the real-time processing of big data is a challenge for the existing GB-SAR interferometry (GB-InSAR) technology. In this paper, we propose a near-real-time GB-InSAR method for monitoring slope surface deformation. The proposed method uses short baseline SAR data to generate interferograms to improve temporal coherence and reduce atmospheric interference. Then, based on the wrapped phase of each interferogram, a network method is used to estimate and remove systematic errors (such as atmospheric delay, radar center shift error, etc.). After the phase unwrapping, a least squares estimator is used for the overall solution to obtain the initial deformation parameters. When new data are added, a sequential estimator is used to combine the previous processing results and dynamically update the deformation parameters. Sequential estimators could avoid repeated calculations and improve data processing efficiency. Finally, the method is validated with the measured data. The results show that the average deviation between the proposed method and the overall estimation was less than 0.01 mm, which could be considered a consistent estimation accuracy. In addition, the calculation time of the sequential estimator was less sensitive than the total amount of data, and the time-consuming growth rate of each additional period of data was about 1/10 of the overall calculation. In summary, the new method could quickly and effectively obtain high-precision surface deformation information and meet the needs of near-real-time slope deformation monitoring. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar technology is highly convenient for monitoring the glacier surface motion in unfavorable areas due to its advantages of being independent of time and weather conditions. A novel glacier motion monitoring method based on the deep matching network (DMN) is proposed in this paper. The network learns the relationship between the glacier SAR image patch-pairs and the corresponding matching labels in an end-to-end manner. Unlike conventional methods that utilize shallow feature tracking, the DMN performs a similarity measurement of deep features, which comprises feature extraction and a metric network. Feature extraction adopts the framework of a Siamese neural network to improve the training efficiency and dense connection blocks to increase the feature utilization. In addition, a self-sample learning method is introduced to generate training samples with matching labels. The experiments are performed on simulated SAR images and real SAR intensity images of the Taku Glacier and the Yanong Glacier, respectively. The results confirm the superiority of the DMN presented in the paper over other methods, even in case of strong noise. Furthermore, a quantitative 2D velocity field of real glaciers is obtained to provide reliable support for high-precision, long-term and large-scale automatic glacier motion monitoring. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) is an important sensor for earth observation. Multi-temporal PolSAR images obtained by successive observations of the region of interest contain rich polarimetric–temporal–spatial information of the land covers, which has wide applications. Speckle filtering becomes a necessary pre-processing for many subsequent applications. Currently, it is common to filter multi-temporal PolSAR data by directly using a speckle filter developed for single SAR or PolSAR data. The cross-correlation between different time series contains rich information in multi-temporal PolSAR images. How to utilize complete polarimetric–temporal–spatial information becomes a large challenge to achieve more satisfied performances of speckle reduction and details preservation simultaneously. This work dedicates to this issue and develops a novel speckle filtering approach for multi-temporal PolSAR data by multi-dimensional information fusion. The core idea is to establish an adaptive and efficient strategy of similar pixel selection based on the similarity test of multi-temporal polarimetric covariance matrices. This similar pixel selection scheme fuses the complete information of multi-temporal PolSAR data. The sensitivity of the proposed scheme is demonstrated with several typical and challenging texture patterns. Then, an adaptive speckle filter is established specifically for multi-temporal PolSAR data. Intensive comparison studies are carried out with airborne UAVSAR datasets and spaceborne ALOS/PALSAR datasets. Quantitative investigations in terms of the equivalent number of looks (ENL) and the figure of merit (FOM) indexes demonstrate and validate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The L band frequency shows high sensitivity to sea surface salinity. More stable brightness temperature (TB) measurements are required for L-band radiometers to reduce salinity retrieval errors than for high-frequency radiometers. Due to the complexity of L-band synthetic aperture radiometers, a carefully selected cold-sky target should be viewed using an L-band synthetic aperture radiometer for the purpose of absolute TB calibration since the celestial sky is relatively well characterized and stable in the L band. A novel, effective cold-sky calibration strategy is presented in this paper. The strategy of cold-sky calibration of the synthetic aperture radiometer is applied when and where the antenna main lobe points to the 'flat' celestial sky, and the impact of each type of foreign source, such as the sun or moon, on visibility values should be minimized in the meantime. Additionally, antenna thermal stability is also considered, which can cause antenna deformation, and the antenna patterns are affected. A high-precision and high-fidelity simulator is built for the cold-sky calibration optimized strategy. The orbital beta angle is introduced to characterize the variation in space environment temperatures. A planet that is considered spherical in shape requires significantly less computation than an ellipsoid one in the simulator. The trade-off study results for the planet shape assumption in the cold-sky calibration simulator are presented. Finally, the calibration uncertainty and performance are assessed. [ABSTRACT FROM AUTHOR]

Airborne synthetic aperture radar (Airborne SAR) can accurately locate key targets and regions by using the flight parameters of the aircraft and the relative position information between the aircraft and the target which can be obtained from the airborne positioning and orientation system (POS). In the course of flight, the aircraft will deviate from the ideal flight path due to atmospheric turbulence, which results in the calculation deviating from the actual target position. In order to improve the target positioning accuracy, it is necessary to study the influence of aircraft motion error on the target positioning error. This study discusses the positioning accuracy of single-view airborne SAR from the perspective of linear Range-Doppler algorithm (RDA), and deduces the multi-view airborne SAR positioning error transfer model based on the multi-view airborne SAR positioning model. Based on these, we analyze the main factors that affect the positioning accuracy of the two positioning methods in detail and quantitatively reveal the mechanism by which the multi-view airborne SAR positioning method can improve the target positioning accuracy compared with the single-view airborne SAR positioning method; we also solve the problem of course planning for multi-view airborne SAR optimal positioning. The research results can provide theoretical support for the analysis of factors influencing positioning error and the positioning error correction of airborne SAR. [ABSTRACT FROM AUTHOR]

Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods. [ABSTRACT FROM AUTHOR]

Environmental effects and climate change are lately representing an increasing strain on coastal areas, whose topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. Continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, and tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relationship with precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar has been a mainstay of the remote sensing field for many years, with a wide range of applications across both civilian and military contexts. However, the lack of openly available datasets of comparable size and quality to those available for optical imagery has severely hampered work on open problems such as automatic target recognition, image understanding and inverse modelling. This paper presents a simulation and analysis framework based on the upgraded SARCASTIC v2.0 engine, along with a selection of case studies demonstrating its application to well-known and novel problems. In particular, we demonstrate that SARCASTIC v2.0 is capable of supporting complex phase-dependent processing such as interferometric height extraction whilst maintaining near-realtime performance on complex scenes. [ABSTRACT FROM AUTHOR]

Ocean salinity is a pivotal aspect of the ocean dynamic environment. Spaceborne L-band radiometers, like SMOS, Aquarius, and SMAP, offer a comprehensive approach to mapping out large-scale ocean salinity patterns. As China prepares for the launch of the Chinese Ocean Salinity and Soil Moisture Mission (COSM), it is essential to delve into the intricacies of radio frequency interference (RFI) detection, localization, and mitigation. The L-band, in particular, is highly susceptible to RFI. COSM will carry not one but two advanced instruments: a 2-D L-band aperture synthesis microwave radiometer (LASMR) and a 1-D L-C-K band microwave imager combined active and passive (MICAP). This article delves into the current state of RFI research, particularly in recent years, and introduces a fusion method that integrates MICAP and LASMR for more accurate RFI detection, localization, and mitigation. This fusion method relies on an algorithm that constructs localization and intensity objective functions based on the principle of least squares. By optimizing these functions, we can pinpoint the precise location and intensity of RFI. The resulting minimum mitigation residual offers a blueprint for achieving optimal RFI detection, localization, and mitigation. The experimental results, achieved in a controlled anechoic chamber, confirm that this fusion method—when weighted by variance—boosts detection accuracy, refines localization precision, and minimizes residual mitigation errors compared with standalone MICAP or LASMR techniques. [ABSTRACT FROM AUTHOR]

This paper proposes an improved substrate integrated coaxial meander delay line to overcome the beam dispersion problem in a spaceborne synthetic aperture radar phased array antenna system with large bandwidth. The delay line operates in the range of 4 GHz–10 GHz with a relative bandwidth of 85.7%. In the proposed structure, the delay effect is generated by the combination of a meander delay line and a substrate integrated coaxial line, which results in a significant improvement in the delay efficiency. Furthermore, a planar turn structure, a vertical transition structure, and a microstrip transition structure are designed to realize three-dimensional tight wiring. The proposed delay lines are fabricated on three and five layered Rogers RO3006 PCB substrate. The experimental tests are conducted on the 3.1 ns and 6.4 ns delay lines with the dimensions of 38 mm × 7 mm × 0.5 mm and 37 mm × 9.2 mm × 1 mm, respectively. The experimental results indicate that the insertion and return losses of the two lines are within the reasonable range. Compared with the related research, the proposed delay lines' delay time, delay multiplier, and relative bandwidth are improved by 2.13–160 times, 1.025–6.8 times, and 2.14–3.86 times, respectively, which shows their good practical value and application prospect. [ABSTRACT FROM AUTHOR]

Ground moving targets will typically be defocused because of the range migration (RM) and Doppler frequency migration (DFM) caused by the unknown relative motions between the platform of synthetic aperture radar (SAR) and the ground moving targets. The received signal of the ground moving target easily exhibits the Doppler ambiguity, and the Doppler ambiguity leads to the refocusing difficulty of ground moving targets. To address these problems, a SAR refocusing method of ground moving targets with Doppler ambiguity based on modified second-order keystone transform (MSOKT) and keystone transform (KT) is presented in this paper. Firstly, the second-order phase is separated by the time reversing process. Secondly, MSOKT is performed to compensate the range curvature migration and DFM, and then the coefficient of the second-order phase is estimated. Finally, a well-refocused result of the moving target is achieved after KT and the estimated Doppler ambiguity number are used to eliminate residual range walk migration. The proposed method can accurately remove RM and DFM and effectively focus the moving targets without residual correction errors. Moreover, the effects of Doppler ambiguity (including Doppler center blur and spectrum split) and blind speed sidelobe are further avoided. On the basis of the analysis of cross-term for the multiple target case, the identification strategy of spurious peak of cross-term is proposed. Additionally, the developed method can be sped up by nonuniform fast Fourier transform without the interpolation operation. The effectiveness of the proposed method is verified by both airborne and spaceborne real data processing results. [ABSTRACT FROM AUTHOR]

Space-based radar sensors have transformed Earth observation since their first use by Seasat in 1978. Radar instruments are less affected by daylight or weather conditions than optical counterparts, suitable for continually monitoring the global biosphere. The current trends in synthetic aperture radar (SAR) platform design are distinct from traditional approaches in that miniaturized satellites carrying SAR are launched in multiples to form a SAR constellation. A systems engineering perspective is presented in this paper to track the transitioning of space-based SAR platforms from large satellites to small satellites. Technological advances therein are analyzed in terms of subsystem components, standalone satellites, and satellite constellations. The availability of commercial satellite constellations, ground stations, and launch services together enable real-time SAR observations with unprecedented details, which will help reveal the global biomass and their changes owing to anthropogenic drivers. The possible roles of small satellites in global biospheric monitoring and the subsequent research areas are also discussed. [ABSTRACT FROM AUTHOR]

The medium-Earth-orbit synthetic aperture radar (MEO-SAR) is deployed at orbit altitude above low-Earth-orbit synthetic aperture radar (LEO-SAR, around 2000 km) and below the geosynchronous orbit SAR (GEO-SAR, near 35786 km) to cover a wide swath, which is four to five times larger than LEO-SAR. Therefore, the measurement method for the LEO-SAR elevation antenna pattern using the SAR data acquired over the Amazon tropical rainforest (ground-based method), where the typical width of rainforest area is approximately 150 km, can hardly meet the requirement of a wide swath to determine the MEO-SAR antenna elevation pattern. Moreover, several new MEO-SAR systems are now proposed that will use low frequency, and the low frequency penetration characteristics may affect the elevation antenna pattern determination using homogenous distributed targets such as the Amazon rainforest. This paper proposes a novel space-based method for the in-orbit measurement of the elevation antenna pattern of MEO-SAR based on one nano calibration satellite mounted with a receiver. Through appropriate orbit design, the nano calibration satellite can fly across the entire MEO-SAR swath along the range direction, and the elevation antenna pattern envelope can be extracted from the data recorded by the receiver. Simulation work is performed to verify the feasibility of the proposed space-based method, and the measurement accuracy of this method is analyzed. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) is a major issue in accurate remote sensing by a synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This paper provides a comprehensive study of the RFI mitigation techniques applicable for an SAR system. From the view of spectrum allocation, possible terrestrial and spaceborne RFI sources to SAR system and their geometry are analyzed. Typical signal models for various RFI types are provided, together with many illustrative examples from real measured data. Then, advanced signal processing techniques for removing RFI are reviewed. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends are provided from the perspective of cognitive, integrated, and adaptive. This review serves as a reference for future work on the implementation of the most suitable RFI mitigation scheme for an air-borne or space-borne SAR system. [ABSTRACT FROM AUTHOR]

In an era of rapid technological improvements, state-of-the-art methodologies and tools dedicated to protecting and promoting our cultural heritage should be developed and extensively employed in the contemporary built environment and lifestyle. At the same time, sustainability principles underline the importance of the continuous use of historic or vernacular buildings as part of the building stock of our society. Adopting a holistic, integrated, multi-disciplinary strategy can link technological innovation with the conservation and restoration of heritage buildings. This paper presents the ongoing research and results of the application of Machine Learning methods for the remote monitoring of the built environment of the historic cluster in Cypriot cities. This study is part of an integrated, multi-scale, and multi-disciplinary study of heritage buildings, with the end goal of creating an online HBIM platform for urban monitoring. [ABSTRACT FROM AUTHOR]

Large infrastructures need continuous maintenance because of materials degradation due to atmospheric agents and their persistent use. This problem makes it imperative to carry out persistent monitoring of infrastructure health conditions in order to guarantee maximum safety at all times. The main issue of early warning infrastructure fault detection is that expensive in-situ distributed monitoring sensor networks have to be installed. On the contrary, the use of satellite data has made it possible to use immediate and low-cost techniques in recent years. In this regard, the potential of spaceborne Synthetic Aperture Radar for the monitoring of critical infrastructures is demonstrated in geographically extended areas, even in the presence of clouds, and in really tough weather. A complete procedure for damage early-warning detection is designed, by using micro-motion (m-m) estimation of critical sites, based on modal proprieties analysis. Particularly, m-m is processed to extract modal features such as natural frequencies and mode shapes generated by vibrations of large infrastructures. Several study cases are here considered and the "Morandi" Bridge (Polcevera Viaduct) in Genoa (Italy) is analyzed in depth highlighting abnormal vibration modes during the period before the bridge collapsed. [ABSTRACT FROM AUTHOR]

The coastal zone offers among the world's most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land- and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and sub-tropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure. [ABSTRACT FROM AUTHOR]

This paper presents the effectiveness of the thermo-mechanical design of a high-speed digital receiver unit (HSDRU) developed for spaceborne synthetic aperture radar applications. The main features of HSDRU's thermo-mechanical design include the thermal management of high-heat dissipation units by adopting heat sinks with the additional function of structural stiffeners and securing the heat rejection path to the upper side of electronics that interfaces the foil radiator for the on-orbit passive thermal control of electronics exposed to deep space environments. The thermal design, which adopts a thermal gap pad, is closely related to the solder joint fatigue life in a launch vibration environment, owing to its initial compressive static load between the heat sink and heat dissipation units that enhances the heat transfer capability. The effectiveness of the design was validated via the qualification level of launch environment tests. [ABSTRACT FROM AUTHOR]

With the development of remote sensing technology and very large-scale integrated circuit (VLSI) technology, the real-time processing of spaceborne Synthetic Aperture Radar (SAR) has greatly improved the ability of Earth observation. However, the characteristics of external memory have led to matrix transposition becoming a technical bottleneck that limits the real-time performance of the SAR imaging system. In order to solve this problem, this paper combines the optimized data mapping method and reasonable hardware architecture to implement a data controller based on the Field-Programmable Gate Array (FPGA). First of all, this paper proposes an optimized dual-channel data storage and access method, so that the two-dimensional data access efficiency can be improved. Then, a hardware architecture is designed with register manager, simplified address generator and dual-channel Double-Data-Rate Three Synchronous Dynamic Random-Access Memory (DDR3 SDRAM) access mode. Finally, the proposed data controller is implemented on the Xilinx XC7VX690T FPGA chip. The experimental results show that the reading efficiency of the data controller proposed is 80% both in the range direction and azimuth direction, and the writing efficiency is 66% both in the range direction and azimuth direction. The results of a comparison with the recent implementations show that the proposed data controller has a higher data bandwidth, is more flexible in its design, and is suitable for use in spaceborne scenarios. [ABSTRACT FROM AUTHOR]

The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore, the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring. [ABSTRACT FROM AUTHOR]

The presence of speckles and the absence of discriminative features make it difficult for the pixel-level polarimetric synthetic aperture radar (PolSAR) image classification to achieve more accurate and coherent interpretation results, especially in the case of limited available training samples. To this end, this paper presents a composite kernel-based elastic net classifier (CK-ENC) for better PolSAR image classification. First, based on superpixel segmentation of different scales, three types of features are extracted to consider more discriminative information, thereby effectively suppressing the interference of speckles and achieving better target contour preservation. Then, a composite kernel (CK) is constructed to map these features and effectively implement feature fusion under the kernel framework. The CK exploits the correlation and diversity between different features to improve the representation and discrimination capabilities of features. Finally, an ENC integrated with CK (CK-ENC) is proposed to achieve better PolSAR image classification performance with limited training samples. Experimental results on airborne and spaceborne PolSAR datasets demonstrate that the proposed CK-ENC can achieve better visual coherence and yield higher classification accuracies than other state-of-art methods, especially in the case of limited training samples. [ABSTRACT FROM AUTHOR]

The P-band spaceborne SAR system faces the problem of a lack of available reference targets for radiometric calibration. Traditional artificial passive calibrators such as trihedral corner reflectors may be hard to be adopted to P-band SAR calibration because of weight, size, manufacture, and installation cost. The parabolic antenna is considered as a potential calibrator due to its stable scattering characteristics and large RCS. One of the criteria of the calibrator is that the beamwidth of the azimuth RCS pattern needs to be much larger than the SAR antenna beamwidth, which is convenient for alignment and obtaining a constant RCS value. It is generally believed that the parabolic antenna can only be used when it is fully aligned to the SAR sensor. However, the P-band spaceborne SAR system usually has a wide beamwidth because of the long wavelength. In contrast, the azimuth RCS pattern of the parabolic antenna has a narrow beamwidth, which will cause inconstant RCS of the calibrator within the synthetic aperture period, thereby introducing an additional calibration error. Due to the imbalance between the SAR antenna beamwidth and the RCS pattern beamwidth, the criterion of using the parabolic antenna as a calibrator may need further discussion. This article analyzes the influence of the RCS pattern of the parabolic antenna on the radiometric calibration and establishes the quantitative error model. Based on the error model, the error analysis is carried out for the two cases of alignment and misalignment. The theoretical analysis shows that the calibration accuracy will decrease if without additional error compensation despite a fully aligned parabolic antenna. Under the case of misalignment, the parabolic antenna can also be used for calibration, even when introducing smaller errors. To verify this conclusion, the RCS pattern of a parabolic antenna is used for the P-band imaging simulation and calibration simulation. The results show that the narrow RCS pattern will introduce a non-negligible error in the calibration when fully aligned. The parabolic antenna can also assist in calibration in the case of misalignment. In addition, the calibration experiment shows that, after compensation with the error model, calibration accuracy has been effectively improved. [ABSTRACT FROM AUTHOR]

The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides' pre-failure behaviour. [ABSTRACT FROM AUTHOR]

Due to the high altitude of geosynchronous synthetic aperture radar (GEO SAR), its synthetic aperture time can reach up to several hundred seconds, and its revisit cycle is very short, which makes it of great application worth in the remote sensing field, such as in disaster monitoring and vegetation measurements. However, because of the elevation of the target, elevation spatial variation error is caused in the GEO SAR imaging. In this paper, we focus on the compensation of the elevation space-variant error in the fast variant part with the autofocus method and utilize the error to carry out elevation inversing in complex scenes. For a complex scene, it can be broken down into a slow variant slope and the remaining fast variant part. First, the phase error caused by the elevation spatial variation is analyzed. Second, the spatial variant error caused by the slowly variant slope is compensated with the improved imaging algorithm. The error caused by the remaining fast variable part is the focus of this paper. We propose a block map-drift phase gradient autofocus (block-MD-PGA) algorithm to compensate for the random phase error part. By dividing sub-blocks reasonably, the elevation spatial variant error is compensated for by an autofocus algorithm in each sub-block. Because the errors of different elevations are diverse, the proposed algorithm is suitable for the scene where the target elevations are almost the same after the sub-blocks are divided. Third, the phase error obtained by the autofocus method is used to inverse the target elevation. Finally, simulations with dot-matrix targets and targets based on the high-resolution TerraSAR-X image verify the excellent effect of the proposed method and the accuracy of the elevation inversion. [ABSTRACT FROM AUTHOR]

Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neural Network (DCNN)-based classifier for training sample generation and coast mitigation. The proposed method has been validated by four measured datasets of NASA/JPL airborne synthetic aperture radar (AIRSAR) and uninhabited aerial vehicle synthetic aperture radar (UAVSAR). Performance comparison with the modified constant false alarm rate (CFAR) detector and the Faster R-CNN has demonstrated that the proposed method can improve the detection probability while reducing the false alarm rate and missed detections. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR image classification, feature extraction technology based on target decomposition is relatively mature, and how to extract discriminative spatial features and integrate these features with polarized information to maximize the classification accuracy is the core issue. In this context, this paper proposes a PolSAR image classification algorithm based on fully convolutional networks (FCNs) and a manifold graph embedding model. First, to describe different types of land objects more comprehensively, various polarized features of PolSAR images are extracted through seven kinds of traditional decomposition methods. Afterwards, drawing on transfer learning, the decomposed features are fed into multiple parallel and pre-trained FCN-8s models to learn deep multi-scale spatial features. Feature maps from the last layer of each FCN model are concatenated to obtain spatial polarization features with high dimensions. Then, a manifold graph embedding model is adopted to seek an effective and compact representation for spatially polarized features in a manifold subspace, simultaneously removing redundant information. Finally, a support vector machine (SVM) is selected as the classifier for pixel-level classification in a manifold subspace. Extensive experiments on three PolSAR datasets demonstrate that the proposed algorithm achieves a superior classification performance. [ABSTRACT FROM AUTHOR]