Search

Objectives: Co-seismic deformation monitoring is of great significance for the interpretation of co-seismic deformation characteristics and intuitive understanding of fault geometric characteristics. For large earthquakes with surface rupture, global navigation satellite system(GNSS) technique has a low spatial resolution, and interferometric synthetic aperture radar(InSAR) technology will cause phase decorrelation due to large deformation gradients, it is impossible to obtain specific deformation around the fault. Optical image correlation(OIC) and pixel offset tracking(POT) based on sub-pixel cross-correlation can solve these problems well. Methods: The main idea of the sub-pixel cross-correlation method is to use the pre-event image in the two images as a reference, and then compare the post-event image with it to evaluate the similarity between the two images, and then retrieve the displacement between the homonymous points. OIC can obtain east-west and north-south deformations, and POT can obtain range and azimuth deformations. In this paper, the deformation in each direction of the Kaikoura earthquake obtained from Sentinel-1 data and Sentinel-2 data is used to form different combinations, and the least square method is used to calculate the three-dimensional deformation. Results: The combination of OIC+POT_As_Des has the best constraint. The accuracy of the north-south deformation obtained by OIC+POT_Range is not as good as that of OIC+POT_As_Des. Compared with POT_As_Des, the north-south deformation precision obtained by it is higher, and the deformation performance on the right side of the Kekerengu fault and the left side of the Papatea fault is also better. In the northeast of the Kaikoura earthquake epicenter, very complex and huge surface deformation and multiple ruptures were detected in two close areas, and the vertical defoamation was mainly uplift. Conclusions: For Sentinel-1 and Sentinel-2, the combination of OIC+ POT_As_Des is most suitable for obtaining the three-dimensional deformation of the Kaikoura earthquake. This earthquake is a dextral strike-slip earthquake with reverse. [ABSTRACT FROM AUTHOR]

Objectives: In 2015, the Mw 7.8 earthquake in Nepal caused landslides and seriously impacted Nepal and its surrounding areas. Methods: In this paper, the Sindhupalchowk area, which is close to the epicenter of the earthquake, is selected as the study area. First, based on the L-band ALOS-2 and C-band Sentinel-1A synthetic aperture radar data, the detection and identification of post-earthquake landslides are carried out by using stacking-InSAR(interferometric synthetic aperture radar) technology. Second, the two-dimensional time series deformation characteristics of a typical landslide are obtained by combining the ascending and descending orbit data and multi-dimensional small baseline subset-InSAR technology. Results: Combined with optical images, 14 hidden points of landslide are delineated. The main deformation of the typical landslide occurs in the horizontal east-west direction, and the maximum deformation rate is −69 mm/a. Conclusions: By analyzing the trend and periodic term deformation signals in time series of the typical landslide, it is found that the earthquake has a significant acceleration effect on the landslide movement, and the increase of rainfall makes the landslide displacement show a periodic change from August to November every year. This paper can provide a reference for post-earthquake landslide monitoring re⁃ search. [ABSTRACT FROM AUTHOR]

Multi-epoch interferometric synthetic aperture radar (InSAR) is a highly effective technique for monitoring deformation in urban areas. However, interpreting InSAR deformation can be challenging due to various factors, including inherent geometric imaging distortion, the intricate structure and deformation properties of targets in urban scenes, and the multiple scattering of microwave signals between objects in urban scenes. This paper discusses the challenges involved in interpreting time-series InSAR deformation: (1) Precisely identifying the location of deformation signals and linking them to their corresponding objects, i. e., determining where the deformation signal occurs, (2) understanding the mechanisms and factors that cause the detected deformation signals, i.e., determining what the deformation signal represents, (3) establishing the connection among the detected deformation signals, the deformation events, and the scattering mechanisms. We suggest a parametric framework to improve the accurate interpretation of InSAR deformation. This framework includes several factors, including kinematic characteristics (deformation rate, cumulative deformation, deformation gradient, and deformation model), geometric parameters (position, size, structure, orientation, and roughness), semantic information (land cover type, terrain morphology, texture, and auxiliary information on natural and anthropogenic disturbance) and physical properties (scattering mechanism, penetrability, extensibility, conductivity, and thermal conductivity). Our approach aims to enhance the representation of coherent points for a better understanding of InSAR deformation. This paper offers a comprehensive overview of the advancements achieved in extracting parameters of InSAR coherent points and interpreting deformation based on geometric parameters, semantic information, and physical properties. High-precision 3D positioning is crucial for InSAR fine monitoring in urban areas. It helps determine the source of deformation signals and facilitates the analysis of deformation mechanisms. Semantic information, such as 3D models, high-resolution optical images, laser point cloud data, and land use data, can aid in interpreting InSAR deformation. By combining InSAR deformation data with a deep learning approach, there is an opportunity to interpret deformations effectively. In urban environments, the scattering mechanism of ground objects is complex. Multiple scattering signals can provide effective observations of deformation and information about the target's size. However, combining the scattering mechanism of synthetic aperture radar signals to carry out parameter inversion and deformation mechanism interpretation of urban target terrain remains a challenge. The framework, which considers the geometric parameters, semantic information, and physical attributes of InSAR coherent points, will be crucial for deformation interpretation and mechanism cognition. This framework will enable fine deformation monitoring, intelligent recognition, and application in future urban scenes. [ABSTRACT FROM AUTHOR]

Objectives: The key phenological information of oilseed rapeseed (Brassica napus L.) plays an important role in field management, viewing time prediction and yield estimation of the oilseed rape. It is also an important part of precision agriculture. Polarimetric synthetic aperture radar technology shows great potential in phenological phase identification with its all-weather monitoring capability and its sensitivity to the crop structural information. Methods: First, we identified the 5 phenological phases of the oilseed rape on the test area with 5 time series full-polarization Radarsat-2 data, which covers the whole growth period of the oilseed rape. 6 typical Stokes parameters are extracted and applied in the identification of oilseed rape phenological phases, the extracted Stokes parameters includ averaged intensity(g0), normalized average intensity(g0m), averaged degree of polarization(ρm), perimeter degree of zero orientation route(Pdor),inclination degree of zero aperture route(Idap), and arc asymmetry degree of zero aperture route(Aadap). Then, The phenological phases of oilseed rape is identified by the decision tree (DT) algorithm based on the comparative analysis of the dynamic response of the 6 special Stokes parameters to rape growth stages. Results and Conclusions: Among the extracted Stokes parameters applied in this study, except ρm and Aadap, other parameters show great sensitivity to the change of the oilseed rape phenological phases. The DT algorithm also perform well in the classification of the oilseed rape phenological phases. The classification results agree well with the field measured samples, and the overall classification accuracy is 87.4%, while the highest classifi⁃ cation accuracy of each phenological phase is 94.3%. [ABSTRACT FROM AUTHOR]

Lots of large hydropower engineering projects have been built in Jinsha River, Lancang River and Yalong River basins in the southwest mountainous areas of China. But the complicated stratum, lithology, structure, hydrology and other geological conditions of these river basins cause widespread, large number and high frequency of reservoir landslide for large hydropower engineering projects. Synthetic aperture radar interferometry(InSAR)technology with the advantages of wide coverage, high monitoring accuracy, and unblocked by clouds, brings new opportunities for early identification and monitoring of reservoir landslide. Especially in recent years, great attention has been paid to the construction of hydropower engineering projects and the reservoir geohazard prevention. The application of InSAR early identification and monitoring of reservoir landslide for large hydropower engineering projects in southwest mountainous area of China was sorted out, and the overall change analysis was carried out from the perspectives of research time, research content and research object. By summarizing the research status and hotspots of InSAR early identification and monitoring of reservoir landslides in southwest mountainous area, it is revealed that Baihetan hydropower engineering project is a milestone and nowadays has entered the outbreak stage of InSAR technology application and research on reservoir landslide. Meanwhile, the different application requirements of InSAR technology and the applicability of InSAR algorithm in the whole life cycle of hydropower engineering project(including the stage before impoundment, impoundment stage and the stage after impoundment)were discussed. In future, with the improvement of SAR data quality and algorithm, InSAR technology will be routinely involved in the identification and monitoring of reservoir landslides throughout the life cycle of hydropower engineering projects. It can provide important support for the early identification, monitoring and early warning, and trigger mechanism research and geohazard prevention of reservoir landslide, so as to enhance the ability of the reservoir geohazard prevention in China. [ABSTRACT FROM AUTHOR]

Objectives: Differential interferometric synthetic aperture radar (D-InSAR) has its own unique monitoring methods in acquiring surface deformation. In view of the difficulties in the application of D-InSAR technology in three-dimensional (3D) deformation monitoring of coal mine surface, this paper proposes a dynamic monitoring method based on fusion of single sight D-InSAR and Boltzmann-Knothe (BK) model. Methods: First, taking into account the characteristics of D-InSAR that it is difficult to obtain large gradient deformations, the Boltzmann function model with good boundary fit was selected, and BK model was constructed by the fusion of the Knothe time function. Second, due to the decoherence of the time-space baseline and the limitation of D-InSAR principle, it can often only obtain the short-term, one-dimensional deformation of the surface in line of sight (LOS). Therefore, based on the projection relationship between the 3D deformation and the LOS deformation, combined with the geological mining conditions of the target area, the fitness function with non-cumulative deformation was constructed. Finally, a dynamic monitoring method of surface 3D deformation of coal mine based on fusion of single sight D-InSAR and BK model was proposed by introducing the fireworks algorithm (FWA). Results and Conclusions: Simulation experiments show that the proposed method can reliably and accurately inverted all dynamic prediction parameters of mining subsidence, and the relative error of the solved parameters was between 0.11% and 7.51%. The subsidence and horizontal movement monitored by this proposed method were consistent with the real values, and the monitoring effect of the PIM-Knothe method was distorted. The results of the anti-error experiment show that under the influence of observation error or model parameter error, the overall effect of the solved parameters by the method in this paper was better, and the ability to resist errors was shown to a certain extent. The proposed method was applied to the 13121 working face of Gubei Mine in Huainan, and the surface 3D deformation of the coal mine from 2019-10-25 to 2019-12-12 was obtained. The conversion of short-term D-InSAR observation to 3D deformation dynamic monitoring based on the proposed method was realized, and the monitoring results were more consistent with the surface movement and deformation measured by the observation station. The reliability and scientificity of the dynamic prediction method for solving the surface 3D deformation of coal seam based on D-InSAR technology has been verified. [ABSTRACT FROM AUTHOR]

Objectives: Interferogram filtering is the key to the subsequent processing steps of interferometric synthetic aperture radar, such as phase unwrapping and geocoding. However, the existing filtering methods can't retain features in dense fringes and accurately estimate phase in low-coherence regions. The convolutional neural networks (CNN) is introduced to learn noise features and solve this problem. Methods: First, we selected a certain number of interferograms as samples, and divided them into training set, test set, and validation set. Second,we preprocessed the training set samples, and cut the preprocessed training set interferograms into small fixed-size block and randomly extract it as a model training sample, used the above steps to train the autoencoder filter model, after a certain number of iterations, the model was fitted. Results: Experiments were carried out on spaceborne imaging radar-C-band synthetic aperture radar data and Sentinel-1A data. Our proposed method was compared with Goldstein filter, mean filter, Lee filter, Frost filter, and improved denoising convolutional neural network (DnCNN). Goldstein filter can remove most of the noise while maintaining fringes edge, has good denoising ability. Mean filter performs well in high-coherence areas, but performs poorly in low-coherence areas, and can't filter noise well. Lee filter maintains it well image resolution, but the denoising effect is weak, and it can be seen from the filtering results that there is still a lot of noise. Frost filtering is weak in low-coherence areas and there is a lot of noise, but the fringe edges are well maintained in high-coherence areas. The improved DnCNN filter can significantly eliminate the noise, but it can't distinguish the fringe edge and the noise well. Our proposed method can suppress the noise very well, and it can maintain the fringe edge well in the low-coherence area and the high-coherence area. Conclusions: This proposed method can greatly improve the phase quality of the interferogram, suppress the noise to a greater extent, and restore more image details and maintain the edge continuity of the interference fringe. [ABSTRACT FROM AUTHOR]

Objectives: Multi-temporal interferometric synthetic aperture radar (InSAR) technology is widely used in deformation monitoring over wide areas due to its characteristics of large-scale, high spatial-temporal resolution and the capability to monitor deformation signal with millimeter precision even submillimeter. For the time-consuming problem in acquiring image and data preprocessing over research area, this paper uses looking inside the continents from space with synthetic aperture radar (LiCSAR) products and takes a new time series analysis LiCSBAS method, which greatly improve the computational efficiency. Methods: The quality check and the closure error of the phase loop are used to detect the unwrapping error in the two-dimensional phase unwrapping, and the interferogram with low coherence and the large error pixels are excluded. To minimize atmospheric artifacts, utilizing the generic atmospheric correction online service for In‑ SAR (GACOS) products and based on this, a method for joint estimation of deformation and atmospheric phase is proposed. Taking the Chengdu Plain and western mountainous area as the study area, totally 90 [ABSTRACT FROM AUTHOR]

By using the self-developed unmanned aerial vehicle (UAV) borne mini synthetic aperture radar (MiniSAR) system, all-directional echoes for multiple types of representative ground targets are collected and used for image processing by the Radar Detection and Imaging Techniques Research Group of Nanjing University of Aeronautics & Astronantics (NUAA). A proprietary SAR database for complex targets is constructed, based on which artificial-intelligence-inspired target recognition approaches are studied. To solve the challenging issue of SAR image defocus caused by unstable movement of the UAV platform and the limited accuracy provided by the accessory sensors, novel movement compensation and auto-focus processing methods are proposed. The impact of the image defocus on the classification accuracies provided by the neural networks is revealed, and the limited generalizability of the existing neural networks is demonstrated. Simulation results show that although the classic neural networks, such as AlexNet, ResNet-18, AConvNet, and VGG offer a near-100% accuracy for the MSTAR 10-target classification problem, the 9-class target classification accuracies provided by these networks for the MiniSAR dataset are all much lower than 90%. Since the experiment method employed in this paper closely resembles the practical application scenario, the proposed database will be of great reference value to the development of SAR target recognition algorithms for engineering applications. [ABSTRACT FROM AUTHOR]

Objectives: Most of the characteristics of land subsidence are analyzed separately from the perspective of temporal or spatial, and the hidden information and possible laws in the data cannot be discovered simultaneously. Temporal principal component analysis (TPCA) can be used to extract temporal and spatial characteristics of temporal and spatial data in the field of geosciences. The land subsidence in the Beijing plain has typical temporal and spatial characteristics. Methods: (1) Permanent scatterer interferometric synthetic aperture radartechnique provide a convenient method to measure land subsidence in sub-centimeter precision. 51 Envisat ASAR data acquired from 2003 to 2010 in the Beijing plain were used to produce 50 interferograms and obtain time-series deformation with nonlinear model. (2) Based on the land subsidence of about 100 000 permanent scatterer (PS) points and 51-time series, we construct the original data matrix X100 000×51, calculate the correlation coefficient matrix, and use the TPCA method to analyze the temporal and spatial evolution characteristics of land subsidence in the Beijing plain. Results: It is found that: (1) The first principal component obtained by TPCA analysis represents the long-term development trend of the spatial distribution of land subsidence. (2) The area that the second principal component that is positive has a correlation in spatial distribution with the area of compressible layer thickness above 130 m. (3) The PS points where the first principal component scores are negative and the second principal component scores are positive are distributed in the severe subsidence area above 30 mm/a. There is an obvious classification of land subsidence and seasonal variation between north and south area in the severe subsidence area. Specifically, in the northern subsidence area, the amount of subsidence in spring and summer is larger than that of in autumn and winter, it is an opposite variation in the southern subsidence area. Conclusions: In general, the temporal and spatial variation of land subsidence could be studied for urban safety monitoring by TPCA. It also can identify the main characteristics of the space and the law of temporal and spatial evolution. Since TPCA is a linear combination, by finding the direction with the largest variance for projection, the variables obtained are just uncorrelated and not independent of each other. Principal component analysis (PCA) only uses the second-order statistical information of the original data and ignores its high-order statistical information. Therefore, it is necessary to optimize by rotating principal components to find more physical meanings of principal components. [ABSTRACT FROM AUTHOR]

Objectives: Few corresponding points can easily affect the calculation of image pose information, increase the difficulty of constructing a regional network in an aerial triangulation solution, so that lead to problems such as image stitching misalignment, incorrect bundle adjustment results or even failure. In order to better complete the matching of unmanned aerial vehicle (UAV) images, this paper proposes a robust UAV image matching algorithm considering log-polar description and position scale distance. Methods: Firstly, a Gaussian multi-scale image collection is established and feature points are extracted. Secondly, the descriptors are constructed using log-polar coordinates, and a descriptor suitable for UAV image characteristics is established. Then, the feature matching is performed by the distance function of position and scale constraints. Finally, the mode seeking and fast sample consensus method are used to eliminate the outliner and complete the extraction of correspondence. Results: The image obtained by four-rotor UAV is used as the data source, and a comparison experiment of image matching with scale invariant feature transform (SIFT) algorithm and synthetic aperture radar-scale invariant feature transform (SARSIFT) algorithm is carried out. The experimental results show that a 210-dimensional log-polar coordinate descriptor is constructed through the gradient location and orientation histogram. The descriptor can better describe the feature points in 10 directions through the circular neighborhood, making the matching results more robust. The position scale Euclidean distance matching function established by integrating factors such as position and scale can better calculate the UAV image matching relationship, and match more correct corresponding points. In terms of the number of correct corresponding points extracted under the same parameter settings, the proposed algorithm is significantly more than the other two algorithms, and in terms of the root mean square error of the matching results, the algorithm in this article is also significantly better than the two compared algorithms. Conclusions: The proposed algorithm can better extract the corresponding points of UAV images. [ABSTRACT FROM AUTHOR]

Objectives: Machine learning classifier‑based water body extraction methods for polarimetric synthetic aperture radar (PolSAR) images have high reliability but typically require a great number of training samples. Consequently, it is very difficult and time‑consuming to manually collect enough training samples when extracting water body from multi‑temporal PolSAR images. To this problem, transfer learning is used to reduce the labor cost of querying new samples and improve the timeliness of water body extraction of multi‑temporal PolSAR images. Methods: Firstly, an optimal source domain image from multi‑temporal images is automatically selected according to the distribution difference between images, and the other images are taken as target domain images. Secondly, a group of training samples are queried in the source domain image as source sample set, and the same number of unlabeled samples are randomly sampled from each target domain image as their target domain sample set. And the knowledge of source domain samples is transferred to target domain samples via the transfer learning method. Finally, a random forest classifier‑based water body extraction model is trained using the target domain sample set, and is used for the water body extraction of target domain images. Results: We have conducted experiments using six PolSAR images and two kinds of transfer learning methods, the results show that:(1) The label transfer accuracy and the water body extraction accuracy are positively correlated. (2) Inductive transfer learning methods achieve higher label transfer accuracy and lower standard deviation. (3) A smaller distribution difference between source and target domain images indicate a greater transferability, and thus a better water body extraction accuracy. (4) The water body extraction results of inductive transfer learning methods have a higher rate of missing detection, while the results of transductive transfer learning methods have a higher rate of false detection. Conclusions: In the water body extraction of multi‑temporal PolSAR images, the use of transfer learning methods can significantly reduce the number of manually labeled samples needed to construct high‑performance classifiers, while maintaining the water body extraction accuracy at a high level. It has great application potentiality in the emergency response of flood disaster. [ABSTRACT FROM AUTHOR]

Objectives: Terrestrial laser scanning (TLS), ground‑based interferometric synthetic aperture radar (GB‑InSAR) and unmanned aerial vehicle(UAV) photography have been applied to emergency deformation monitoring of rockslide. The objectives are concerns about new problems of 3D visualization of deformation data. Methods: Firstly, we introduced iterative closest point method (ICP) to complete the registration between the TLS point cloud and UAV terrain model. Secondly, geometric mapping method was applied to achieve data fusion between GB‑InSAR deformation map and TLS point cloud. The mapping deviation is difficult to be corrected by conventional methods, because the lack of artificial control points on the rockslide. Visual interpretation and GB‑InSAR images simulation method were combined to extract the control points. The spatial coordinate transformation parameters were estimated with the least square method. The transformation model was established to correct the mismatch. Results: The proposed method was verified by the simulation data and the actual monitoring data of a landslide. The result shows that the accuracy reaches the pixel level which meet the needs of emergency monitoring. Conclusions: The proposed method is limited by the working conditions of emergency and the subjectivity of image interpretation. If the accuracy of control point selection was not narrowed, the final matching fusion position accuracy may reach pixel‑level accuracy. [ABSTRACT FROM AUTHOR]

Objectives: On January 8th 2022, an Ms 6.9 earthquake struck Menyuan, Qinghai Province, China. The epicenter is located in the middle section of Qilian-Haiyuan fault in the northeast margin of the Tibetan Plateau, which is a seismic gap. It is of great significance to study this earthquake for under-standing the tectonic deformation mechanism, strain release process and seismic hazards of the NE Tibetan Plateau.Methods: Differential synthetic aperture radar interferometry (D-InSAR) is used to obtain the coseismic deformation along the line of sight (LOS) of the Menyuan earthquake from Sentinel-1 satellite images. The co-registration of optically sensed images and correlation are used to obtain the EW coseismic deformation from the multi-temporal Sentinel-2 data. Based on the coseismic deformation field derived from InSAR interferograms, we invert the slip distribution. Results: The LOS deformation fields showed that the Menyuan earthquake caused a surface rupture of about 20 km long and the maximum LOS displacement was 0.75 m. The maximum EW displacement derived from optical images was 2.5 m. The slip distribution model showed that the Menyuan earthquake was dominated by a sinistral strike-slip. The coseismic slip is concentrated at a depth of 0 - 10 km. The maximum slip was 3.25 m at a depth of 4.89 km and the rake angle was 10.44°. The estimated total seismic moment is 1.07x1019 N.m, corresponding to a magnitude of Mw 6.6. Conclusions: According to the results from aftershock relocation, field investigation and tectonic background, we infer that the seismogenic fault is the Lenglongling fault and the eastern segment of Tuolaishan fault is also ruptured. The stress on the eastern segment of the Lenglongling fault and the western segment of the Tuolaishan fault is enhanced, implying that there is the possibility of strong earthquakes in this region in the future. [ABSTRACT FROM AUTHOR]

Objectives: Two earthquakes with a distance less than 40 km occurred in Menyuan, Qinghai Province, China, including Mw 5.9 earthquake on 26th January 2016 and Mw 6.7 earthquake on 8th January 2022. Methods: The interferometric synthetic aperture radar (InSAR) technique was utilized to process Sentinel-1A radar images from both ascending and descending tracks to retrieve high resolution coseismic surface displacement maps for these two Menyuan earthquakes. The coseismic surface displacements were then inverted to determine the fault geometry parameters and the non-uniform fault slip distributions with a dislocation model in an elastic half- space. Results: The results show that the 2016 Menyuan earthquake was a reverse event on a buried fault with a maximum surface displacement of 6.7 cm and 7.0 cm on the ascending and descending tracks, respectively. The maximum slip on the fault plane reached 0.53 m and concentrated at depths of 4 − 12 km. The 2022 Menyuan earthquake ruptured along the NWW - SEE strike with a maximum surface displacement of 78 cm in the satellite radar line of sight and a maximum fault slip of 3.5 m occurred at a depth of approximately 4 km. Conclusions: Considering the tectonic settings around the Lenglongling fault zone, the 2022 Menyuan earthquake is likely to occure in the western segment of the left‐lateral Lenglongling fault, with its western end extending westward to the Tuolaishan fault. By calculating the coulomb failure stress changes (CFS), it is inferred that the 2016 Menyuan earthquake generated considerable positive CFS which might have triggered the 2022 Menyuan earthquake. [ABSTRACT FROM AUTHOR]

Objectives: Synthetic aperture sonar (SAS) is one kind of high resolution underwater imaging sonar. Its principle is to use small array to simulate large aperture array by uniforming linear motion along the direction of flight track, and realize coherent processing of echo signal to obtain high resolution two-dimensional sonar image, which is independent of imaging distance and acoustic wavelength. The back projection algorithm is an accurate point-by-point imaging algorithm, which is characterized by accurate focusing ability, suitable for wide beam and large scene imaging, and easy to realize motion compensation, but it has the disadvantages of low computational efficiency. In recent years, with the development of computer technology, especially the appearance of graphics processor unit, it has powerful computing power and provides a new way to accelerate the back projection algorithm. Methods: We propose a fast accurate back projection algorithm for multi-receiver SAS in heterogeneous environment. On the basis of analyzing the principle of accurate back-projection imaging algorithm, the pulse compression and time-consuming azi-muth accumulation are transformed into a single instruction multi-threaded mode, and the imaging process is accelerated by the powerful multi-core computing ability of graphics processor unit. Results: The validity and efficiency of the proposed fast imaging algorithm are verified by the imaging experiments performed on simulated and real SAS data. Compared with the serial imaging algorithm, its acceleration ratio is as high as 326.3 and 333.6. For large-scale data imaging processing, it shows excellent acceleration performance. Conclusions: The proposed back projection imaging algorithm greatly improves the imaging efficiency and meets the needs of real-time SAS imaging, which lays the foundation for subsequent motion compensation. [ABSTRACT FROM AUTHOR]

Objectives: Integrated remote sensing application based on the space-air-ground investigation system has been promoted nationwide and has achieved certain results. However, as the work progresses, some urgent problems have been exposed, such as how to define hidden hazards and what types of them, what are the targeted identification technologies, and what is the focus of the next step, etc. Methods: By summarizing a large number of practical cases, the definition of hidden landslide hazards is further clarified. Potential landslides are divided into three categories: Deformation area, historical deformation failure area and potentially unstable slope, and we propose targeted identification techniques for different types. Results: The deformation zone is mainly identified by combining multi-temporal high resolution optical images and synthetic aperture radar interferometry technology (InSAR), high-resolution optical remote sensing images and light detection and ranging (LiDAR) can be used in the historical deformation and destruction region. Conclusions: Potential unstable slopes are difficult to identify by remote sensing and traditional artificial investigations. It is necessary to vigorously develop aerial and semi-aerial geophysical prospecting technology to identify the underground structure of the slope quickly, especially the spatial distribution of the base-cover interface and the groundwater status, and delineate dangerous areas through quantitative analysis of stability. [ABSTRACT FROM AUTHOR]

Objectives：Traditional echo simulator of synthetic aperture sonar (SAS) is based on the assumption that transmitter and receiver beam patterns are rectangle within mainlobe, which is determined by 3 dB beamwidth of beam pattern. This neglects the echo signal outside the mainlobe. Besides, the modulation of transmitter and receiver beam patterns is not considered. Methods：Depending on the instantaneous geometry relationship among transmitter, receiver and target, the echo simulation of SAS considering the modulation of transmitter and receiver beam patterns is carried out. Then, the influence of transmitter and receiver beam patterns on SAS imaging is discussed by the back projection algorithm. Results and Conslusions: Traditional array signal processing based on weighting is further conducted to show the influence of transmitter and receiver beam patterns on imaging results. The results are useful for the suppression of spectrum alias in the azimuth direction. Moreover, they are also helpful for the simulation verification and analysis of performance parameters of designed SAS systems. [ABSTRACT FROM AUTHOR]

Objectives: In the research of time-series interferometric synthetic aperture radar (TS-InSAR) deformation monitoring, the conventional triangular reflectors are increasingly used as point targets. But the conventional triangular reflectors cannot meet the requirements of obtaining multi-angle deformation information on ascending and descending track scenes. They need to be tailored for specific datasets to meet monitoring needs. Methods: A set schemes for corner reflector design and accuracy verification are constructed. According to the characteristics of X-band radar satellite satellite, a two-way adjustable rectangular corner reflector is designed to meet the monitoring scene of the ascending and descending track. Due to the relationship between the radar cross section(RCS) and the size of the corner reflector, the side length of the bidirectional rectangular corner reflector is calculated to be 0.35 m. Ten bidirectional rectangular corner reflectors are placed on the side slope of the highway in the monitoring area. By artificially simulating the two-dimensional deformation, the detection accuracy and reliability of the TS-InSAR on the bidirectional rectangular corner reflector is verified. Results: Under the condition of effectively eliminating the sidelobe effect of the pixel, the designed bidirectional rectangular corner reflector maintains a high and stable backscatter ing intensity, which can be easily extracted. Through the analysis of the weather conditions at the time of imaging, heavy rainfall has a certain influence on the backscattering intensity of corner reflectors. Under the blind experimental conditions of artificially adjusting the height of the corner reflector, CR1 and CR2 are moved between May 13, 2018 and May 24, 2018. The root mean square error is 0.6 mm. The deformation detection accuracy reaches the sub-millimeter level. Conclusions: The TS-InSAR is highly sensitive to the deformation of the designed bidirectional rectangular corner reflector. The bidirectional rectangular corner reflector can meet the requirements of obtaining multi-angle deformation information on ascending and descending track scenes. [ABSTRACT FROM AUTHOR]

Objectives: Complex image registration is crucial in interferometric synthetic aperture sonar (InSAS) signal processing. The registration quality directly influences the generation of interferogram and the subsequent reconstruction of digital elevation model. The traditional global polynomial surface fitting method has the disadvantage of low accuracy for large scene InSAS images. Therefore, a registration method based on piecewise surface fitting is proposed to overcome the problem. Methods: Firstly, the whole image is partitioned into several pieces along range direction with partly overlapped. Then, polynomial surface fitting is performed using control points in each piece. Finally, the global offset surface is obtained by merging those local surface fitting results. Results: Through the simulation and real data experiments, the accuracy of complex image registration using the proposed method is higher than the result of global polynomial surface fitting method. Conclusions: The evaluation has been carried out with the root mean squared error, correlation coefficient, residue numbers, which indicates the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

Objectives: The mountain excavation and city construction in Yan’an New District is one of the largest land‑creation projects in the Loess Plateau. Large‑scale mountain excavation has induced extensive land uplift in the region. At the same time, the excavation area is the main site for the urbanization of the new district, and the land uplift has seriously affected the sustainable development of urbanization in the new district. Methods: In this study, the small baseline subset synthetic aperture radar interferometry (SBAS‑InSAR) technology was utilized to process the ascending Sentinel‑1A data stacks covering the new district from May 2016 to October 2019 to retrieve long ‑ term ground deformation information after construction. The spatial pattern of ground uplift and its time deformation evolution characteristics was revealed, and the causes of land uplift were further quantitatively analyzed in detail. Results: The results show that significant land uplift of the new district after construction is mainly distributed in the excavation area that has the greatest impact on human engineering activities, with a maximum uplift rate of 24.8 mm/a. The ground lifting deformation develops rapidly in the early post‑construction period, and it tends to slow down and stabilize over time.Conclusions: The unloading rebound deformation caused by the rapid mountain excavation is the main internal factor causing land uplifting, and the distribution and magnitude of the land uplifting are the result of the combined effects of external factors such as excavation engineering, excavation thickness, and building load. [ABSTRACT FROM AUTHOR]

Objectives: Yan’an New District has attracted much attention because of its large‑scale mountain excavation and city construction project. Such mega‑scale land creation, rapid urban construction and complex geological conditions has induced wide land subsidence in the region. Methods: In this paper, the temporal and spatial evolution of land subsidence in Yan’ an New District was assessed using small baseline subset synthetic aperture radar interferometry (SBAS‑InSAR) to process the Sentinel‑1A images covering the period from May 2016 to October 2019. Then, the mechanism and evolution trend of land subsidence in the new district was analyzed. Results: Experimental results suggest that the spatial evolution pattern of land subsidence in Yan’an New District is highly relevant with the process of land creation project. The land subsidence area in the new district gradually expanded from the major urban area to the area of forest along with the land creation project, and its area decreased gradually with the passing of time. The temporal deformation evolution of land subsidence in the new district is related to the thickness of the fill, and all exhibit non‑linear subsidence with various velocities. The temporal evolution of land subsidence undergoes three processes of rapid‑slow‑steady. The greater the thickness of the fill, the larger the amount of sedimentation, the longer it takes for the sedimentation to stabilize. Conclusions: The main internal mechanism of land subsidence is the consolidation and compression deformation of the loess fill, which can be divided into three stages: Instantaneous settlement, consolidation settlement, and secondary consolidation settlement. Based on the characteristics of different stages combined with time series deformation, the land subsidence process and development trend can be analyzed. The research results provide a scientific reference for further monitoring and early warning, urban planning, and prevention and control of subsidence disasters in the new district. [ABSTRACT FROM AUTHOR]

This paper constructed a new TerraSAR-X dataset named GDUT-Nansha and then proposed a new lightweight semantic segmentation algorithm using synthetic aperture radar images（ SAR）. It is difficult to apply traditional deep learning models to SAR datasets with small volume due to the tremendous amount of parameters. A revision scheme is, therefore, put forward to deal with such problem. This paper accomplished an improved lightweight convolutional neural network model named revised weighted loss ENet（ RWL-ENet） for SAR images. The current study introduced a weighted loss function to solve the problem of imbalance of training datasets. Compared with other classical convolution neural network models, the efficiency and robustness of the new dataset were validated. Meanwhile, RWL-ENet model attained 0. 884,0. 804, and 0. 645 in terms of three quantitative metrics, including pixel accuracy（ PA）, mean pixel accuracy（ m PA）, and mean intersection over union（ m IoU）. In addition, the parameters of this new proposed model are much less than other classic network models. [ABSTRACT FROM AUTHOR]

In order to reduce the multiplicative speckle noise of synthetic aperture radar SAR image. an improved non-local mean (NLM) algorithm is proposed in this paper. Firstly, the coefficient of variation (CV) was used to estimate the SAR image region division threshold and estimate area division factor based on threshold. Thus. the SAR image was divided into texture area and homogeneous area by area division factor. Then. similarity windows of different scales were used to estimate the ESM corresponding to pixels in different regions, and the ESM matrix of each pixel in the search window under the similarity window and the ESM matrix of the center pixel were selected further. Moreover. the two-norm of the ratio of the two corresponding elements were used to construct the similarity measurement parameter that adapts to the multiplicative noise. filter value was obtained by combining the similarity parameter with the adaptive de cay factor dominated by CV. Finally, the non local average weight filtering of the SAR image was realized by different regions with different filtering values. Compared with NLM filtering algorithm based on the mean ratio and the coefficient of variation. the proposed algorithm performs better in that the equivalent numbers of looks increased by more than 0,3% and edge preservation index increased by more than 0.1% [ABSTRACT FROM AUTHOR]

Objectives: On 8th August 2017, a catastrophic earthquake of Ms 7.0 struck the County of Jiu-zhaigou in Sichuan Province, China. Because of its high magnitude and shallow epicenter, the earthquake caused grave casualties and property losses. Furthermore, the earthquake triggered numerous secondary mountain disasters such as rockfall and landslide. We use the synthetic aperture radar interferometry (In-SAR) technique to detect post-earthquake landslides in Jiuzhaigou area. Methods: Firstly, we carry out a quick detection across wide area using differential InSAR (DInSAR) technique with 6 ALOS-2 PALSAR-2 ascending images. DInSAR technique can observe the subtle deformation of landslide surface during the pe-riod between two SAR observations, so the active deforming landslides can be efficiently distinguished from stable areas. Nevertheless, the performance of DInSAR is often limited by temporal and spatial decor-relations induced by dense vegetation coverage, etc. To retrieve the temporal evolution of these landslides, detailed monitoring of specific landslides is carried out using time-series InSAR technique with 112 Sentinel-1 ascending and descending images. Time-series InSAR technique can overcome the limitation of tradi-tional DInSAR technique in spatial-temporal decorrelation, and improve the deformation measurement accuracy. Therefore, the combination of DInSAR and time-series InSAR is adopted to accurately monitor sin-gle landslide, and efficiently detect landslides in Jiuzhaigou area. Results: The results show that there are 13 landslides that can be detected by InSAR analyses in Jiuzhaigou area, including 7 landslides close to residential areas. The time series displacements retrieved indicate that several landslides have long-term linear deformations after the 2017 earthquake. These landslides severely threaten people's life and property and need to be monitored regularly. Finally, the reliability of detection results based on InSAR technology is verified by a field survey. Conclusions: The results can provide key supports for the geohazard mitigation and prevention in the Jiuzhaigou scenic area. We indicate that a strategy which combines DInSAR technique with time-series InSAR can efficiently and accurately detect and monitor landslides over a wide area. [ABSTRACT FROM AUTHOR]

Aiming at the serious problem of de - coherence in the forest classification of the C - band synthetic aperture radar im¬age in conjunction with the optical image, a method of averaging coherence coefficient images in multiple periods in winter obtained by coherent imaging of similar time - phase radar images is proposed to effectively suppress the de - coherence phenomenon to obtain co¬herence coefficient images related to forest tree species. This paper uses the Sentinel - 1 backscattering intensity, coherence coefficient and JL101A image to classify the tree species of the moon lake national forest park in Changchun. The results show that when using on¬ly JL101A for classification, the overall classification accuracy is 82. 3% , Kappa coefficient is 0. 79； when using JL101A data and Sentinel - 1 intensity data for classification, the overall classification accuracy is 85.2% , and the Kappa coefficient is 0. 825 ； when u- sing JL101A data, Sentinel -1 intensity data and coherence data for classification, the overall classification accuracy is 87. 8% , and the Kappa coefficient is 0. 855. After using JL101A data, Sentinel - 1 intensity data and coherence data to classify, compared with u- sing only JL101A data, the precision of user accuracy is increased from 59% to 72%. It shows that the optical image combined with the effective coherence coefficient and backscattering intensity of C band synthetic aperture radar image can improve the classification accuracy of forest tree species. [ABSTRACT FROM AUTHOR]

Aiming at the key problem of low precision of the deformation data due to the inaccurate correction of the original image interference phase error based on the ground-based synthetic aperture radar(GB-SAR) in open pit slope， the sources and distribution characteristics of interference phase error in original image were analyzed with the original GB-SAR image of Malanzhuang open-pit iron mine as the data source. The method of extracting PS points and high-quality PS points by using triple threshold was also proposed. In addition， a multi-regression model was established based on the high-quality PS point phase and pixel coordinates. According to the model， the PS point phase was corrected to obtain the accurate deformation phase and the single image deformation， and the superposition deformation analysis was performed on the time series. The results show that the GB-SAR deformation monitoring error correction method based on multiple regression model can accurately correct the GB-SAR deformation monitoring error and improve the accuracy of deformation monitoring data. [ABSTRACT FROM AUTHOR]

Sea clutter is studied and analyzed for evaluating the accuracy of missile-borne synthetic aperture radar under different sea conditions. A parameter estimation method based on BP neural network is proposed to solve the problem that the sea clutter parameter estimation based on traditional statistical methods is prone to be divorced from the actual sea clutter. The amplitude distribution characteristics and temporal correlation of sea clutter are used to establish a K-distribution-based sea clutter model. The influences of four model parameters, such as shape, scale, mean square root of clutter speed, and mean Doppler shift, on sea clutter chaos and fractal characteristics are analyzed, and the qualitative relationship among model parameters and physical characteristics is summarized. On this basis, BP neural network is used to fully explore the quantitative relationship among parameters and physical characteristics, and predict the chaos characteristics and fractal characteristics. The determinate coefficients are 0.985 and 0.952. The model parameters of BP neural network, maximum likelihood estimation and moment estimation method are compared by taking the measured sea clutter data as an example. The results show that the proposed model can be well close to the physical characteristics of actual sea clutter. [ABSTRACT FROM AUTHOR]

With Loffeld’s bistatic formula (LBF), the transfer function of the multireceiver synthetic aperture sonar (SAS) can be decomposed into the quasi-monostatic term and bistatic deformation term. For this model, the phase error in the 2D frequency domain is quantitatively analyzed. The results indicate that this model can fully satisfy the imaging need with high performance. Based on that, a new imaging method is proposed. Firstly,the range variant term of bistatic deformation phase should be first compensated in the 2D frequency domain via the range-dependent sub-block processing. Subsequently, the monostatic SAS equivalent data can be obtained by rearranging the multireceiver data in order in the 2D time domain. Then, a range-Doppler (R-D) algorithm is exploited to process the monostatic SAS equivalent data. Finally, the processing results of simulated and real data validate the proposed method. [ABSTRACT FROM AUTHOR]

We propose a combined minimum discontinuity phase unwrapping algorithm based on clusters. The wrapped phase image is tessellated into small regular blocks by the main node according to the com - puting resources and then sent to the idle computing nodes. During the unwrapping process of single block, the wrapped phase quality map is firstly computed and the wrapped phase image is partitioned into high and low quality areas; then the quality-guided and minimum discontinuity combined phase unwrapping algorithm is performed to get the initial unwrapped result. After the local unwrapping process is finished, the initial unwrapped result and the region partition result are sent to the main node. When all the blocked phases are unwrapped, the main node performs the minimum discontinuity optimization process on the bor- der of different blocks and the adjacent low quality areas to get the final unwrapped result. Tests performed on the simulated and the real InSAS (interferometric synthetic aperture sonar) interferograms based on clus- ters confirm the validity and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Complex image registration is one of the most important steps during the process of interferometric signal processing, and its efficiency directly affects the performance of interferometric signal processing system. In order to improve the efficiency of complex image registration algorithm, we propose an opti⁃ mized complex image registration algorithm for interferometric synthetic aperture sonar (InSAS) in shared memory environment. Firstly, after analyzing the characteristic of calculation and parallelism of each pro⁃ cessing step for complex image registration, an optimized computation method for coarse registration and refine registration is proposed for the massive sliding operation based on the coherence of data in adjacent windows. Then, in order to make full use of multi-core computing resource to accelerate the process of complex image registration, OpenMP instructions are used to parallel design and task allocation for coarse and refine registration, complex image interpolation and wrapped phase extraction. Finally, the correctness and efficiency of the proposed method are verified by the parallel complex image registration for InSAS. [ABSTRACT FROM AUTHOR]

In order to promote the wider use of ground-based synthetic aperture radar (GB-SAR) in the field of surface micro-deformation monitoring, this paper summarizes the main types of GB-SAR and introduces the working principles and important parameters of some representative systems. By taking the S-SAR (slope-synthetic aperture radar) ground-based radar independently developed by the China Academy of Safety and Production Science as an example，the key processing techniques utilized by the GB-SAR are introduced. Experiments verify the accuracy of GB-SAR. It also shows the application of GB-SAR in open pit mine slope monitoring，mine dumping site and landslide emergency rescue. Finally，the problems and suggestions of GB-SAR in monitoring are proposed. [ABSTRACT FROM AUTHOR]

The theory and method of mining subsidence monitoring based on D-InSAR(differential interferometric synthetic aperture radar) technology is a hot topic in the current field of mine deformation monitoring. At present, three-dimensional monitoring model of mining subsidence has been constructed successfully by some scholars through integrating single line of sight to D-InSAR technology and mining subsidence law. However, the model is only suitable for the researches on mining subsidence of horizontal coal seam rather than inclined coal seam. In view of the above defects，according to the relationship between deformation of line of sight monitored by D-InSAR and three-dimensional deformation, considering the surface subsidence law of inclined coal seam mining, the surface subsidence monitoring method for inclined coal seam based on single line of sight to D-InSAR technology is proposed. The simulation experiment shows that the absolute relative error of the subsidence monitoring method is less than 9. 53%, with an average of 1. 31%. The absolute relative error of the horizontal movement monitoring along North-South (SN) and East-West (EW) is less than 9. 78%，with an ave-rage of 3. 71%, which meets the mining subsidence monitoring accuracy requirements. The surface movement and deformation caused by the mining of the 9310 working face of Nantun coal mine in yanzhou mining area, Shandong province China during 2012-01-27 - 2012-02-07 are successfully monitored by this method in this paper [ABSTRACT FROM AUTHOR]

Complex image registration is a crucial step in interferometric synthetic aperture sonar (InSAS) signal processing. The registration quality directly influences the generation of interferogram and subsequent reconstruction of digital elevation model (DEM). Utilizing the geometric model of imaging, the rational function relationship between offset and slant range is deduced, thus this paper propose a new InSAS complex image registration method based on second order rational function surface fitting. Compared with the traditional polynomial surface fitting method, the proposed method in this paper has the advantages of higher precision and smaller calculation cost. The evaluation has been carried out using the root mean squared error(RMSE), correlation coefficient,residue numbers and computing time as the criteria. Simulation and real data experiments results indicate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposed an efficient range Doppler imaging algorithm for multi-array synthetic aperture sonar (SAS) in shared memory environment based on OpenMP, which solved the imaging problem of low efficiency by multi-core CPU resources. Considered the parallel characteristic of range Doppler algorithm for multi-array SAS, it parallelized the signal processing process such as preprocessing, range compression, phase compensation, range cell migration compensation and azimuth compression based on OpenMP, which greatly enhanced the reconstruction process of SAS image by multi-core CPU resources. The imaging test performed on real data shows that the speedup of the parallel imaging algorithm is up to 19. 86 times, which meets the demand for real-time processing of SAS imaging. [ABSTRACT FROM AUTHOR]

Aiming to solve the low efficiency imaging of multi-array synthetic aperture sonar, this paper proposed a fast imaging method for multi-array synthetic aperture sonar in heterogeneous environment. It considered the range Doppler algorithm characteristic for multi-array synthetic aperture sonar and different calculation features between CPU and GPU, the intensive calculation such as range compression, phase compensation, range cell migration compensation and azimuth compression was implemented on GPU, which greatly enhanced the imaging efficiency for multi-array synthetic aperture sonar. In the end, it verifies the validity and efficiency of the proposed method through the imaging experiment performed on real data and the speedup is up to 14.45 times compared to the serial algorithm. [ABSTRACT FROM AUTHOR]

Because of good property of superimposing the received signals from adjacent channels, the synthetic aperture focusing technique (SAFT) can further improve the signal characteristic of defects, which has been widely used in ultrasonic nondestructive testing (NDT). For the concrete blocks embedded with defects in relatively deep position, it's hard to obtain the high-resolution imaging results using the traditional B-Scan method and the traditional SAFT method because of the weak reflection information caused by heavy attenuation. In order to improve the ultrasonic imaging quality of concrete, the relative attenuation coefficient of ultrasonic propagating in concrete is measured, and the amplitude compensation technique is introduced to enhance the weal ultrasonic echo. Then the compensated signals are processed using the SAFT method, and the ultrasonic imaging quality can be further improved. Artificial concrete experiments show that the proposed method has higher imaging resolution than the traditional imaging methods, and the reflections of defects at the bottom area are clearer. The experimental results, including the embedded iron pipe and wooden block, verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A time-variant curve (TVC) model-based automatic equalization method is proposed for the intensity variation problem of synthetic aperture sonar (SAS) images. A theoretical expression of TVC is deduced based on sound transmission model, underwater backscattering strength model and SAS image model. A method to calculate the TVC observations is established based on the statistical model of SAS images. An estimation method based on non-linear least square fitting model (NL-LSFM) is used to acquire the optimized parameter of TVC. At last, the SAS images are automatically equalized and enhanced based on the optimized TVC. The method proposed has been validated by lake and sea trials. The test results show that the result calculated by the TVC expression is consistent with the experimental data, and the automatic equalization method can remove the intensity variation of SAS images properly. [ABSTRACT FROM AUTHOR]

Drew lesson from the synthetic aperture radar, and made the further processing to the fictitious wave-field data, that was correlation and synthesizing processing. Specifically, developed the traditional mode of processing that mainly based on profiles into a new method,in which, several apertures synthesis with the measure point as the center, lapse point-by-point, finally, multiple coverage was adopted. In hopes to improves the signal-to-noise ratio, highlight the abnormal, improves the resolution and increases the exploration depth. Through the processing of theoretical model data, verified the effectiveness of this method. The different theoretical model processing suggests that related to enhanced synthesis of useful signal, to improve the signal to noise ratio, resolution enhancement effect. The feasibility and validity of synthetic aperture of transient electromagnetic imaging numerical calculation was demonstrated. [ABSTRACT FROM AUTHOR]