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Mine slope landslides seriously threaten the safety of people's lives and property in mining areas. Landslide prediction is an effective way to reduce losses due to such disasters. In recent years, micro-deformation monitoring radar has been widely used in mine slope landslide monitoring. However, traditional landslide prediction methods are not able to make full use of the diversified monitoring data from these radars. This paper proposes a landslide time prediction method based on the time series monitoring data of micro-deformation monitoring radar. Specifically, deformation displacement, coherence and deformation volume, and the parametric degree of deformation (DOD) are calculated and combined with the use of the tangent angle method. Finally, the effectiveness of the method is verified by using measured data of a landslide in a mining area. The experimental results show that our proposed method can be used to identify the characteristics of an imminent sliding slope and landslide in advance, providing monitoring personnel with more reliable landslide prediction results. [ABSTRACT FROM AUTHOR]

In contemporary security detection systems, the utilization of millimeter-wave radar has assumed a central role owing to its non-contact and innocuous nature. This study addresses the challenging issues of detecting low-resolution and small targets in active millimeter wave (AMMW) images concealed detection. We identify a prevalent drawback in existing detectors, specifically the adoption of strided convolution or pooling layers, leading to a loss of crucial details that adversely impacts the detection rate. To overcome this limitation, we introduce a novel convolutional structure, termed Wavelet-Conv, which maintains information integrity while segregating features from high and low frequency bands, effectively replacing the unfavorable design. Furthermore, we harness the wavelet transform to enhance channel and spatial attention mechanisms, enabling more effective utilization of frequency band features and ensuring interpretability in the computational process. In this pursuit, we integrate the proposed Wavelet-Conv and Wavelet-Attention modules into the YOLOv8 framework, culminating in a unified model, termed Wavelet-YOLO. Through rigorous experimental validation on two AMMW datasets, our approach exhibits superior performance by significantly enhancing the recall and mean average precision (mAP) of small targets in AMMW images, while maintaining competitive inference speed. Extensive experiments demonstrate the outperformance of our proposed method over existing state-of-the-art approaches. • Enhancing object detection in Active Millimeter Wave Images. • Utilizing DWT for feature dimensionality reduction in convolutional networks. • Leveraging wavelet transform for enhanced spatial and channel attention. • Improving small target detection in blurred images with wavelet-based approaches. [ABSTRACT FROM AUTHOR]

Remote sensing image object detection is a challenging task in the field of computer vision due to the complex backgrounds and diverse arrangements of targets in remote sensing images, forming intricate scenes. To overcome this challenge, existing object detection models adopt rotated target detection methods. However, these methods often lead to a loss of semantic information during feature extraction, specifically regarding the insufficient consideration of element correlations. To solve this problem, this research introduces a novel attention module (EuPea) designed to effectively capture inter-elemental information in feature maps and generate more powerful feature maps for use in neural networks. In the EuPea attention mechanism, we integrate distance information and Pearson correlation coefficient information between elements in the feature map. Experimental results show that using either type of information individually can improve network performance, but their combination has a stronger effect, producing an attention-weighted feature map. This improvement effectively enhances the object detection performance of the model, enabling it to better comprehend information in remote sensing images. Concurrently, this also improves missed detections and false alarms in object detection. Experimental results obtained on the DOTA, NWPU VHR-10, and DIOR datasets indicate that, compared with baseline RCNN models, our approach achieves respective improvements of 1.0%, 2.4%, and 1.8% in mean average precision (mAP). [ABSTRACT FROM AUTHOR]

• A zoning method based on reactive power-voltage sensitivity weights and active network loss-voltage sensitivity weights is proposed. • This method is based on the established modularity function model for partitioning. • Based on the two weight partitioning results, the concept of aggregation domain is proposed. • Voltage control is performed from the aggregation domain results obtained above. As the scale of the power system continues to expand, it becomes more difficult to regulate the voltage of large grids in a unified manner by traditional control methods. In this paper, based on the theory of modularity function model in complex networks, a voltage coordination control method of partitioning the aggregated domain of reactive-voltage sensitivity weights and active network loss-voltage sensitivity weights of power systems is proposed. Firstly, the system is partitioned according to the modularity function model and the concept of aggregation domain is proposed. Secondly, the reasonableness of the aggregation domain is judged by using the proposed reactive power support capability index, and the partitioned voltage control strategy is proposed to realize the optimal and reasonable control of the voltage crossing point. Finally, the simulation experiment compares the voltage regulation results of the centralized control method and the zonal control method. The zoned control method provides a relative voltage improvement of 3.27% over the centralized control method, the active network loss of the zoned control method is 3.30% less than that of the centralized control method, and a relative reduction in time of use of 90.70%. The simulation results show that the proposed method can effectively reduce the search range of reactive power compensation devices, reduce the number of nodes for coordinated control, reduce the active network loss of the system due to voltage regulation, and ensure the control capability of reactive power compensation devices within the partition. [ABSTRACT FROM AUTHOR]

Airborne downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) operates downward-looking observation and obtains the 3-D microwave scattering information of the observed scene. The cross-track physical sparse linear array is often configured to obtain uniform virtual phase centers in order to adopt the frequency-domain algorithm. However, the virtual phase centers usually have to be nonuniformly and sparsely distributed due to the array elements' installation locations restricted by the airborne platform and the airborne wing tremor effect. In this state, the frequency-domain algorithm cannot be directly used. In this paper, a DLSLA 3-D SAR image reconstruction algorithm that combines polar formatting and $L_1$ regularization is presented. Wave propagation and along-track dimensional imaging are first finished after polar formatting and wavefront curvature phase error compensation; then, cross-track dimensional imaging is completed with the $L_1$ regularization technique. The proposed algorithm is applicable to airborne DLSLA 3-D SAR imaging under nonuniformly and sparsely distributed virtual phase centers condition. The proposed algorithm was verified by 3-D distributed scene simulation experiment (P-band circular SAR image was selected as radar cross-section input, and X-band digital elevation model of the same area was selected as the coordinate positions of the scene) and the field experiment. Image reconstruction results and image reconstruction performances, such as normalized radar cross section, height errors, and orthographic projection image grayscale distribution, are demonstrated and analyzed with different signal-to-noise ratios, different array sparsity, and the incomplete compensated residual oscillation error 3-D distributed scene simulation experiments. Simulation and field experimental results show the good performance in focusing and the robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the gradual expansion of mining scale in open-pit coal mines, slope safety problems are increasingly diversified and complicated. In order to reduce the potential loss caused by slope sliding and reduce the major threat to the safety of life and property of residents in the mining area, this study selected two mining areas in Xinjiang as cases and focused on the relationship between phase noise and deformation. The study predicts the specific time point of slope sliding by analyzing the dynamic history correlation tangent angle between the two. Firstly, the time series data of the micro-variation monitoring radar are used to obtain the small deformation of the study area by differential InSAR (D-InSAR), and the phase noise is extracted from the radar echo in the sequence data. Then, the volume of the deformation body is calculated by analyzing the small deformation at each time point, and the standard deviation of the phase noise is calculated accordingly. Finally, the sliding time of the deformation body is predicted by combining the tangent angle of the ratio of the volume of the deformation body to the standard deviation of the phase noise. The results show that the maximum deformation rates of the deformation bodies in the studied mining areas reach 10.1 mm/h and 6.65 mm/h, respectively, and the maximum deformation volumes are 2,619,521.74 mm3 and 2,503,794.206 mm3, respectively. The predicted landslide time is earlier than the actual landslide time, which verifies the effectiveness of the proposed method. This prediction method can effectively identify the upcoming sliding events and the characteristics of the slope, provide more accurate and reliable prediction results for the slope monitoring staff, and significantly improve the efficiency of slope monitoring and early warning. [ABSTRACT FROM AUTHOR]

The study of the polarimetric target decomposition algorithm with physical scattering models has contributed to the development of the field of remote sensing because of its simple and clear physical meaning with a small computational effort. However, most of the volume scattering models in these algorithms are for forests or crops, and there is a lack of volume scattering models for grasslands. In order to improve the accuracy of the polarimetric target decomposition algorithm adapted to grassland data, in this paper, a novel volume scattering model is derived considering the characteristics of real grassland plant structure and combined with the backward scattering coefficients of grass, which is abstracted as a rotatable ellipsoid of variable shape. In the process of rotation, the possibility of rotation is considered in two dimensions, the tilt angle and canting angle; for particle shape, the anisotropy degree A is directly introduced as a parameter to describe and expand the applicability of the model at the same time. After obtaining the analytical solution of the parameters and using the principle of least negative power to determine the optimal solution of the model, the algorithm is validated by applying it to the C-band AirBorne dataset of Hunshandak grassland in Inner Mongolia and the X-band Cosmos-Skymed dataset of Xiwuqi grassland in Inner Mongolia. The performance of the algorithm with five polarimetric target decomposition algorithms is studied comparatively. The experimental results show that the algorithm proposed in this paper outperforms the other algorithms in terms of grassland decomposition accuracy on different bands of data. [ABSTRACT FROM AUTHOR]

Azimuth multi-channel synthetic aperture radar (SAR) has always been an important technical means to achieve high-resolution wide-swath (HRWS) SAR imaging. However, in the space-borne azimuth multi-channel SAR system, random phase noise will be produced during the operation of each channel receiver. The phase noise of each channel is superimposed on the SAR echo signal of the corresponding channel, which will cause the phase imbalance between the channels and lead to the generation of false targets. In view of the above problems, this paper proposes a random phase noise compensation method for space-borne azimuth multi-channel SAR. This method performs feature decomposition by calculating the covariance matrix of the echo signal and converts the random phase noise estimation into the optimal solution of the cost function. Considering that the phase noise in the receiver has frequency-dependent and time-varying characteristics, this method calculates the phase noise estimation value corresponding to each range-frequency point in the range direction and obtains the phase noise estimation value by expectation in the azimuth direction. The proposed random phase noise compensation method can suppress false targets well and make the radar present a well-focused SAR image. Finally, the usefulness of the suggested method is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

When using ground-based synthetic aperture radar (GB-SAR) for monitoring open-pit mines, dynamic atmospheric conditions can interfere with the propagation speed of electromagnetic waves, resulting in atmospheric phase errors. These errors are particularly complex in rapidly changing weather conditions or steep terrain, significantly impacting monitoring accuracy. In such scenarios, traditional regression model-based atmospheric phase correction (APC) methods often become unsuitable. To address this issue, this paper proposes a clustering method based on the spatial autocorrelation function. First, the interferogram is uniformly divided into multiple blocks, and the phase consistency of each block is evaluated using the spatial autocorrelation function. Then, a region growing algorithm is employed to classify each block according to its phase pattern, followed by merging adjacent blocks based on statistical data. To verify the feasibility of the proposed method, both the traditional regression model-based method and the proposed method were applied to deformation monitoring of an open-pit mine in Northwest China. The experimental results show that for complex atmospheric phase scenarios, the proposed method significantly outperformed traditional methods, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

Arc array synthetic aperture radar (AA-SAR), which can observe the scene in all directions, breaks through the single view of traditional SAR. However, the concealment of AA-SAR is poor. To mitigate this, arc array bistatic SAR (AA-BiSAR) with the moving transmitter is proposed, it has the advantages of good concealment and can expand the imaging scene, and improve the flexibility of the system. The imaging geometry including the signal model is established, and a range frequency-domain algorithm based on keystone transform (KT) is proposed in this paper. In the first step, the slant range equation is approximated by Taylor series expansion to compensate for the residual phase caused by the transmitter motion. In the second step, the range cell migration between the range and azimuth is eliminated through the KT method in the range frequency-domain. In the third step, using the data after range cell migration correction in step 2, an azimuth pulse compression is performed to obtain the focused image. In addition, the spatial resolution of the AA-BiSAR system is analyzed in detail. Finally, three simulation results verify the effectiveness of the proposed algorithm and the change in the spatial resolution. [ABSTRACT FROM AUTHOR]

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification. [ABSTRACT FROM AUTHOR]

As remote sensing methods have received a lot of attention, ground-based micro- deformation monitoring radars have been widely used in recent years due to their wide range, high accuracy, and all-day monitoring capability. On the one hand, these monitoring radars break through the limitations of traditional point monitoring equipment such as the Global Navigation Satellite System (GNSS) and fissure meters in terms of monitoring scope and ease of installation. On the other hand, the data types of these monitoring radars are more varied. Therefore, it may be difficult for the data-processing method of traditional point monitoring equipment to take all advantages of this type of radar. In this paper, based on time-series monitoring data of ground-based micro-deformation monitoring radars, three parameters—extent of change (EOC), extent of stability (EOS), and extent of mutation (EOM)—are calculated according to deformation value, coherence and deformation pixels size. Then a method for landslide prediction by combining these three parameters with the inverse velocity method is proposed. The effectiveness of this method is verified by the measured data of a landslide in Yunnan Province, China. The experimental results show that the method can correctly discern deformation areas and provide more accurate monitoring results, especially when the deformation trend changes rapidly. In summary, this method can improve the response rate and prediction accuracy in extreme cases, such as rapid deformation. [ABSTRACT FROM AUTHOR]

In recent years, the development of neural networks has significantly advanced their application in Synthetic Aperture Radar (SAR) ship target detection for maritime traffic control and ship management. However, traditional neural network architectures are often complex and resource intensive, making them unsuitable for deployment on artificial satellites. To address this issue, this paper proposes a lightweight neural network: the Multi-Scale SAR Ship Detection Network (MSSD-Net). Initially, the MobileOne network module is employed to construct the backbone network for feature extraction from SAR images. Subsequently, a Multi-Scale Coordinate Attention (MSCA) module is designed to enhance the network's capability to process contextual information. This is followed by the integration of features across different scales using an FPN + PAN structure. Lastly, an Anchor-Free approach is utilized for the rapid detection of ship targets. To evaluate the performance of MSSD-Net, we conducted extensive experiments on the Synthetic Aperture Radar Ship Detection Dataset (SSDD) and SAR-Ship-Dataset. Our experimental results demonstrate that MSSD-Net achieves a mean average precision (mAP) of 98.02% on the SSDD while maintaining a compact model size of only 1.635 million parameters. This indicates that MSSD-Net effectively reduces model complexity without compromising its ability to achieve high accuracy in object detection tasks. [ABSTRACT FROM AUTHOR]

Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data. [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]

Compared to conventional radars, arc array synthetic aperture radar (SAR) enables wide-area observation under ideal conditions. However, helicopters carrying arc array SAR platforms are generally smaller in size and more sensitive to vibration, which has a greater impact on the imaging quality. In this paper, the vibration error of the arc array SAR platform is investigated, and a vibration error model of the arc array SAR platform is established. Based on the study of the vibration error model, a vibration phase estimation and compensation algorithm based on the delayed conjugate multiplication method is proposed. In the first step, distance pulse pressure processing is performed on the echo signal. In the second step, the pulse pressure signals and their delays in the same distance unit are subjected to conjugate multiplication, and the phase of the signal after conjugate multiplication is extracted. The extracted phase is then amplitude- and phase-compensated to estimate the vibration phase. In the third step, the vibration phase is compensated in the azimuthal direction of the distance pulse pressure signal, and the pairwise echo is eliminated, which completes the compensation of the airborne arc array SAR vibration platform. [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]

Digital beamforming with null steering in elevation can be used to separate echoes corresponding to different sub-pulses, but the pulse extension loss will decrease performance on echo separation. To resolve this problem, an improved null steering beamformer based on multi-group time delays is proposed. In the proposed beamformer, the phase weighting matrix is multiplied and the multi-group time delays are used to improve the coherence between echoes from different channels. Then, the modified phase weighing matrix for null steering is introduced to further improve the capacity of echo separation. Compared with the conventional null steering beamformer, the multi-group time delays can provide the more accurate time delay compensation for better echo separation. Simulation results on both point and distributed targets validate the proposed beamformer. [ABSTRACT FROM AUTHOR]

Over-exploitation of coal mines leads to surface subsidence, surface cracks, collapses, landslides, and other geological disasters. Taking a mining area in Nalintaohai Town, Ejin Horo Banner, Ordos City, Inner Mongolia Autonomous Region, as an example, Sentinel-1A data from January 2018 to October 2019 were used as the data source in this study. Based on the high interference coherence of the permanent scatterer (PS) over a long period of time, the problem of the manual selection of ground control points (GCPs) affecting the monitoring results during refinement and re-flattening is solved. A DInSAR-PS-Stacking method combining the PS three-threshold method (the coherence coefficient threshold, amplitude dispersion index threshold, and deformation velocity interval) is proposed as a means to select ground control points for refinement and re-flattening, as well as a means to obtain time-series deformation by weighted stacking processing. A SBAS-PS-InSAR method combining the PS three-threshold method to select PS points as GCPs for refinement and re-flattening is also proposed. The surface deformation results monitored by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods are analyzed and verified. The results show that the subsidence location, range, distribution, and space–time subsidence law of surface deformation results obtained by DInSAR-PS-Stacking, SBAS-PS-InSAR, and GPS methods are basically the same. The deformation results obtained by these two InSAR methods have a good correlation with the GPS monitoring results, and the MAE and RMSE are within the acceptable range. The error showed that the edge of the subsidence basin was small and that the center was large. Both methods were found to be able to effectively monitor the coal mine, but there were also shortcomings. DInSAR-PS-Stacking has a strong ability to monitor the settlement center. SBAS-PS-InSAR performed well in monitoring slow and small deformations, but its monitoring of the settlement center was insufficient. Considering the advantages of these two InSAR methods, we proposed fusing the time-series deformation results obtained using these two InSAR methods to allow for more reliable deformation results and to carry out settlement analysis. The results showed that the automatic two-threshold (deformation threshold and average coherence threshold) fusion was effective for monitoring and analysis, and the deformation monitoring results are in good agreement with the actual situation. The deformation information obtained by the comparison, and fusion of multiple methods can allow for better monitoring and analysis of the mining area surface deformation, and can also provide a scientific reference for mining subsidence control and early disaster warning. [ABSTRACT FROM AUTHOR]

An arc array synthetic aperture radar (AA-SAR) is a new type of omnidirectional observation and imaging system. Based on linear array 3D imaging, this paper introduces a keystone algorithm combined with the arc array SAR 2D imaging method and proposes a modified 3D imaging algorithm based on keystone transformation. The first step is to discuss the target azimuth angle, retain the far-field approximation method of the first-order term, analyze the influence of the forward motion of the platform on the along-track position, and realize the two-dimensional focusing of the target slant range–azimuth direction. The second step is to redefine a new azimuth angle variable in the slant-range along-track imaging and use the keystone-based processing algorithm in the range frequency domain to eliminate the coupling term generated by the array angle and the slant-range time. The corrected data are used to perform along-track pulse compression to obtain the focused image of the target and realize the three-dimensional imaging of the target. Finally, in this article, the spatial resolution of the AA-SAR system in the forward-looking state is analyzed in detail, and the change in the spatial resolution of the system and the effectiveness of the algorithm are verified through simulation. [ABSTRACT FROM AUTHOR]

Change detection is an important application of remote sensing image interpretation, which identifies changed areas of interest from a pair of bi-temporal remote sensing images. Various deep-learning-based approaches have demonstrated promising results and most of these models used an encoder–decoder shape such as U-Net for segmentation of changed areas. In order to obtain more refined features, this paper introduces a change detection model with cascaded U-Net. The proposed network architecture contains four cascaded U-Nets with ConvNeXT blocks. With a patch embedding layer, the cascaded structure can improve detection results with acceptable computational overhead. To facilitate the training of the cascaded N-Nets, we proposed a novel attention mechanism called the Training whEel Attention Module (TEAM). During the training phase, TEAM aggregates outputs from different stages of cascaded structures and shifts attention from outputs from shallow stages to outputs from deeper stages. The experimental results show that our cascaded U-Net architecture with TEAM achieves state-of-the-art performance in two change detection datasets without extra training data. [ABSTRACT FROM AUTHOR]

The block varying pulse repetition frequency (BV-PRF) scheme applied to spaceborne squint sliding-spotlight synthetic aperture radar (SAR) can resolve large-range cell migration (RCM) and reduce azimuth signal non-uniformity. However, in the BV-PRF scheme, different raw data blocks have different PRFs, and the raw data in each block are insufficiently sampled. To resolve the two problems, a novel azimuth full-aperture pre-processing method is proposed to handle the SAR raw data formed by the BV-PRF scheme. The key point of the approach is the resampling of block data with different PRFs and the continuous splicing of azimuth data. The method mainly consists of four parts: de-skewing, resampling, azimuth continuous combination, and Doppler history recovery. After de-skewing, the raw data with different PRFs can be resampled individually to obtain a uniform azimuth sampling interval, and an appropriate azimuth time shift is introduced to ensure the continuous combination of the azimuth signal. Consequently, the resulting raw data are sufficiently and uniformly sampled in azimuth, which could be well handled by classical SAR-focusing algorithms. Simulation results on point targets validate the proposed azimuth pre-processing approach. Furthermore, compared with methods to process SAR data with continuous PRF, the proposed method is more effective. [ABSTRACT FROM AUTHOR]

Previous studies have shown that scattering mechanism ambiguity and negative power issues still exist in model-based polarization target decomposition algorithms, even though deorientation processing is implemented. One possible reason for this is that the dynamic range of the model itself is limited and cannot fully satisfy the mixed scenario. To address these problems, we propose a hybrid polarimetric target decomposition algorithm (GRH) with a generalized volume scattering model (GVSM) and a random particle cloud volume scattering model (RPCM). The adaptive volume scattering model used in GRH incorporates GVSM and RPCM to model the volume scattering model of the regions dominated by double-bounce scattering and the surface scattering, respectively, to expand the dynamic range of the model. In addition, GRH selects the volume scattering component between GVSM and RPCM adaptively according to the target dominant scattering mechanism of fully polarimetric synthetic aperture radar (PolSAR) data. The effectiveness of the proposed method was demonstrated using AirSAR dataset over San Francisco. Comparison studies were carried out to test the performance of GRH over several target decomposition algorithms. Experimental results show that the GRH outperforms the algorithms we tested in this study in decomposition accuracy and reduces the number of pixels with negative powers, demonstrating that the GRH can significantly avoid mechanism ambiguity and negative power issues. [ABSTRACT FROM AUTHOR]

Staggered synthetic aperture radar (SAR) can effectively stagger range blind areas by periodically changing the pulse repetition interval (PRI), to achieve continuous imaging with an ultrawide swath. In conventional staggered SAR, azimuth samples are nonuniformly distributed due to the variable PRI, and complex resampling processing, such as best linear unbiased (BLU) interpolation and multichannel reconstruction processing, is required. In this article, a strict rule of PRI variation and phase center adjustment (PCA) is proposed for staggered SAR, to solve the problem of nonuniform azimuth sampling. In the proposed joint strategy of PRI variation and PCA, the relevant parameters for PCA are first determined. Then, the PRI sequence is designed according to PCA parameters to stagger blind ranges. Finally, the PCA rule is designed according to the designed PRI sequence and PCA parameters. During the data acquisition interval, the effective phase center is periodically adjusted pulse by pulse according to this rule, making the azimuth samples totally uniformly distributed. In addition, the discontinuously distributed missing samples can be estimated by signal estimation approaches. With the proposed joint rule of PRI variation and PCA, the emergence of false targets in imaging results can be avoided, and the complex resampling processing can be relieved to reduce the computational complexity. Simulation experiments on imaging results verify the advantages of this strategy. [ABSTRACT FROM AUTHOR]

To acquire high-resolution wide-swath (HRWS) imaging capacity, the displaced phase center multichannel azimuth beam (DPCMAB) technology is usually adopted in spaceborne synthetic aperture radar (SAR), while multichannel reconstruction must be carried out before imaging process due to azimuth nonuniform sampling. Up to now, almost all azimuth multichannel reconstruction algorithms have been mainly based on conventional hyperbolic range equation (CHRE), but the accuracy of the CHRE model is usually not suitable for the HRWS mode, especially for high resolution and large squint observation cases. In this study, the azimuth multichannel signal model based on the advanced hyperbolic range equation (AHRE) is established and analyzed. The major difference between multichannel signal models based on CHRE and AHRE is the additional time-varying phase error between azimuth channels. The time-varying phase error is small and can be ignored in the monostatic DPCMAB SAR system, but it must be considered and compensated in the distributed DPCMAB SAR system. In addition to the time-varying phase error, additional Doppler spectrum shift and extended Doppler bandwidth should be considered in the squint case during azimuth multichannel reconstruction. The azimuth multichannel reconstruction algorithm based on AHRE is proposed in this paper. Before multichannel reconstruction and combination, time-varying phase errors between azimuth channels were first compensated, and the range-frequency-dependent de-skewing function was derived to remove the two-dimension (2D) spectrum tilt to avoid azimuth under-sampling. Then, azimuth multichannel data were reconstructed according to the azimuth multichannel impulse response based on AHRE. Finally, the range-frequency dependent re-skewing function was introduced to recover the tilted 2D spectrum. Simulation results on both point and distributed targets validated the proposed azimuth multichannel reconstruction approach. [ABSTRACT FROM AUTHOR]

Scan-on-receive (SCORE) digital beamforming (DBF) in elevation can significantly improve the signal-to-noise ratio (SNR) and suppress range ambiguities in spaceborne synthetic aperture radar (SAR). It has been identified as one of the important methods to obtain high-resolution wide-swath (HRWS) SAR images. However, with the improvement of geometric resolution and swath width, the residual pulse extension loss (PEL) due to the long pulse duration in the conventional spaceborne onboard DBF processor must be considered and reduced. In this paper, according to the imaging geometry of the spaceborne DBF SAR system, the reason for the large attenuation of the receiving gain at the edge of the wide swath is analyzed, and two improved onboard DBF methods to mitigate the receive gain loss are given and analyzed. Taking account of both the advantages and drawbacks of the two improved DBF methods presented, a novel onboard DBF processor with multi-frequency and multi-group time delays in HRWS SAR is proposed. Compared with the DBF processor only with multi-group time delays, the downlink data rate was clearly reduced, while focusing performance degradation due to phase and amplitude errors between different frequency bands could be mitigated compared with the DBF processor only with multi-frequency time delays. The simulation results of both point and distributed targets validate the proposed DBF processor. [ABSTRACT FROM AUTHOR]

In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to acquire azimuth high geometric resolution, and its two-dimensional (2D) impulse response with the low SLL is usually obtained from the 2D weighted power spectral density (PSD) by the selected weighting window function. However, this results in the SNR reduction due to 2D amplitude window weighting. In this paper, the staring spotlight SAR with nonlinear frequency modulation (NLFM) signal and azimuth non-uniform sampling (ANUS) is proposed to obtain high geometric resolution SAR images with the low SLL and almost without any SNR reduction. The NLFM signal obtains non-equal interval frequency sampling points under uniform time sampling by adjusting the instantaneous chirp rate. Its corresponding PSD is similar to the weighting window function, and its pulse compression result without amplitude window weighting has low sidelobes. To obtain a similar Doppler frequency distribution for low sidelobe imaging in azimuth, the received SAR echoes are designed to be non-uniformly sampled in azimuth, in which the sampling sequence is dense in middle and sparse in both ends, and azimuth compression result with window weighting would also have low sidelobes. According to the echo model of the proposed imaging mode, both the back projection algorithm (BPA) and range migration algorithm (RMA) are modified and presented to handle the raw data of the proposed imaging mode. Both imaging results on simulated targets and experimental real SAR data processing results of a ground-based radar validate the proposed low sidelobe imaging mode. [ABSTRACT FROM AUTHOR]

A synthetic aperture radar (SAR) system can be seriously contaminated by radio frequency systems because of working in the same microwave frequency bands, which would degrade the SAR image quality and affect the accuracy of image interpretation. In this paper, a novel radio frequency interference (RFI) suppression approach including RFI identification, band-stop filtering and a removed spectrum iterative adaptive approach (RSIAA) is proposed. First, the smoothing process is added before RFI signal detection to improve the RFI detection capacity. Afterwards, the band-stop filtering with a broaden factor is proposed to mitigate the residual RFI, and it ensures the accuracy of the following removed spectrum recovery by the RSIAA. Finally, the removed spectrum components are estimated from available adjacent spectrum data by the RSIAA in turn to obtain the desired range spectra. Compared with the conventional range frequency filtering method for RFI suppression, the capacity of the weak RFI signal detection is improved, and the increased sidelobes due to the discontinuous spectra are well suppressed. Simulation experiments on both simulated SAR raw data, Gaofen-3 and Sentinel-1 SAR raw data validate the proposed RFI suppression approach. [ABSTRACT FROM AUTHOR]

In a multichannel geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) system, the airborne receiver can obtain high-resolution microwave images with good signal-to-noise ratios (SNRs) by passively receiving echoes from the desired area. Since the Doppler modulation and range history of a moving target are obviously different from a stationary target, a signal geometry model for moving targets in multichannel GEO-SA-BiSAR is established in this paper. According to simulation results, the along track velocity introduces target defocusing in azimuth, and the slant range velocity mainly causes multiple false targets. To resolve these problems, a modified multichannel reconstruction method in azimuth channel GEO-SA-BiSAR is proposed according to the azimuth multichannel impulse response of the imaged moving target. Before azimuth multichannel raw data combination, both spatial-variant range cell migration correction (RCMC) and azimuth nonlinear chirp scaling (ANLCS) should be performed to reduce the influence of the range offset and lower the Doppler bandwidth of the whole raw data, respectively. Afterward, a novel azimuth multichannel reconstruction algorithm is carried out via the modified reconstruction matrix based on the estimated target velocity. The target slant range velocity estimation is implemented by introducing the signal intensity ratio (SIR). Compared with the conventional method for the stationary target to handle the raw data of the moving target, the false targets could be obviously suppressed by using the proposed approach. Imaging results on both simulated point and distributed scene targets validate the proposed multichannel reconstruction approach. [ABSTRACT FROM AUTHOR]

A spaceborne azimuth multichannel synthetic aperture radar (SAR) system can effectively realize high resolution wide swath (HRWS) imaging. However, the performance of this system is restricted by its two inherent defects. Firstly, non-uniform sampling is generated if the pulse repetition frequency (PRF) deviates from the optimum value. Secondly, multichannel systems are very sensitive to channel errors, which are difficult to completely eliminate. In this paper, we propose a novel receive antenna architecture with an azimuth phase center adaptive adjustment which adjusts the phase center position of each sub-aperture to improve multichannel SAR system performance. On one hand, the optimum value of the PRF can be adaptively adjusted within a certain range by adjusting receiving phase centers to obtain uniform azimuth sampling. On the other hand, false targets introduced by residual channel errors after azimuth multichannel error compensation can be further suppressed. The effectiveness of the proposed method to compensate for non-uniform sampling and suppress false targets is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

Ground-based synthetic aperture radar (GB-SAR) uses active microwave remote-sensing observation mode to achieve two-dimensional deformation measurement and deformation trend extraction, which shows great prospects in the field of deformation monitoring. However, in the process of GB-SAR deformation monitoring, the disturbances caused by atmospheric effect cannot be neglected, and the atmospheric phases will seriously affect the precision of deformation monitoring. In discontinuous GB-SAR deformation monitoring mode, the atmospheric phases are particularly affected by changes of time and space, so the traditional models of atmospheric phase correction are no longer applicable. In this paper, the interferometric phase signal model considering atmospheric phase is first established. Then, the time- and space-varying characteristics of the atmospheric phase are analyzed, and a novel time- and space-varying atmospheric phase correction algorithm, based on coherent scatterers analysis, is proposed. Finally, slope deformation monitoring experiments are carried out to verify the validity and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]