Search

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In order to solve the problems of low efficiency, multiple blind spots, and high risks in traditional debris flow exploration, two technical means were adopted: small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) and unmanned aerial vehicle (UAV) low altitude remote sensing (the former obtains highly reliable deformation of vegetation coverage areas through dense images, while the latter obtains high-resolution realistic 3D models through regional network adjustment and other processing) . Integrating deformation and image texture, a survey and analysis of debris flow valleys along a railway in a certain mountainous area was conducted to obtain the spatial and temporal distribution of geological hazards in the watershed. The results show that, through SBAS-InSAR technology, it was found that there were multiple seasonal active hazards in the debris flow valley, with a maximum deformation variable of 28.5 mm/a. Based on the 3D model of UAV, a detailed investigation was conducted on the spatial form, volume, occurrence, and development of the deformation hazard. The largest scale landslide had a volume of 165 000 to 180 000 cubic meters. On site investigations show that the comprehensive remote sensing technology can effectively overcome the challenges of complex mountainous terrain and difficult access for humans, providing a multidimensional perspective for disaster screening. It has the advantages of low personnel investment, wide monitoring area, fast disaster positioning, and high screening accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] Syntheticaperture radar (SAR) is an active observation technology distinguished by its all-time and all-weather monitoring capability. Consequently, SAR systems offer distinctive advantages in disaster monitoring, environmental surveillance, and mapping applications. However, given the intricacies of the SAR system, data acquisition is confined to mobile platforms, such as aircraftand satellites. This limitation poses challenges to the teaching of the SAR course as it requires hands-on experiments. To address this issue, an experimental platform for the virtual simulation of SAR high-resolution imaging was designed and developed. [Methods] This platform includes an experimental business, application presentation, logic control, and data analysis layers. The experimental business layer includes three modules: System design and data acquisition, signal processing, and evaluation and application. Each module includes 2--4 submodules. The progressive design according to the actual field experiment process of the SAR can restore the experimental design, equipment operation, data processing, and other processes. The application presentation layer provides students with an immersive experience and guarantees ateaching effect. The logic control layer provides convenience for human--computer interaction and enhances students' sense of participation in the experiment. The data analysis layer can analyze and reproduce experimental problems to consolidate basic knowledge. The virtual simulation experiment comprises system design, experiment planning, data acquisition, signal processing, and image evaluation. Through computer simulation, the entire SAR experiment is simulated with high display, fidelity, and interaction. At each simulation stage, students can emulate the characteristics of the simulation experiment, including high model reproducibility, repeatability, ease of interaction, and safety. In the system design and experiment planning stage, students can intuitively and swiftly adjust the installation position of system components and arrange the experiment scenes. In the signal processing stage, students can exercise control over the simulation process through modular programming on the simulation platform. In the evaluation stage, students can visually assess the experimental results using the built-in graphical programming of the simulation platform. Notably, a comprehensive process analysis and feedback mechanism have been devised to assist students in accurately identifying their shortcomings and promptly rectifying them. [Results] The outcomes of this experimental platform are outlined as follows: 1 The realistic simulation of the actual scene of the experiment is performed to deepen students' understanding of the basic knowledge of the SAR system. Concurrently, the mechanisms of step prompt, after-class feedback, and evaluation are established to help students understand their shortcomings and make improvements in a timely fashion. 2 The operation, design, and processing of the actual experiments are reproduced, providing students with opportunities to practice and explore that are not easily accessible in reality, helping students learn SAR technology and improve their professional and technical abilities. 3 The teaching method combining simulation and practical operation is established. This open experimental design can improve students' innovation ability and comprehension quality. [Conclusions] The platform has completed several rounds of service teaching and is open to the public online. After background information sorting and course feedback, the platform has been unanimously praised, which can significantly improve students' theoretical learning ability, innovation ability, and exploration consciousness. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A new water surface perception system based on ultrasonic phased array imaging technology has been designed, which can achieve the perception and imaging of target objects in the nearshore sea environment. The ultrasonic wave is emitted by the transmitting transducer, and the echo signal is received by the receiving transducer. After being amplified by an amplification circuit, a sliding average filtering algorithm is introduced for data smoothing processing. The ADC acquisition module is used to convert it into a digital signal for time delay estimation. Then, the target object is imaged using a time delay superposition algorithm, and the Canny operator edge detection is performed. Finally, the size and orientation of the target are obtained. The experiment verified the reliability of hardware circuits and the effectiveness of software algorithms, achieving the design goals of low cost, high portability, and high reliability, and meeting the teaching requirements. [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]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the zero-shot learning and recognition problem of stealth targets for inverse synthetic aperture imaging radar (ISAR), the existed algorithms cannot recognize the new category of targets that have not appeared in the training process. A target learning and recognition method with zero-shot learning (ZSL) is proposed for solving the problem above. Firstly, based on three dimensional grid physical model of the aircraft-class dynamic target, the FEKO electromagnetic field simulation software programming is used to realize the ISAR-visible source target image data generation. Secondly, on this basis, the text semantic feature expression of detail attributes for different aircraft-class targets is formed. In the novel-type proposed algorithm's network model, two variation autoencoders are used to generate image and semantic features respectively, so that the network can learn the modal's invariant feature expression. The network is trained with visible category data to obtain the model that can generate image features based on semantic information. The training recognition process adopts the targets of the new category which are unknown and invisible for the proposed learning model. The unknown and invisible image feature information of the new target is generated from the invisible and unknown text semantic of the new category, which supports the recognition for the new targets which are unknown and invisible. The accuracy rate of the recognition for the new category targets is up to 75% according to the relevant statistic result, which means an important military and strategic significance. [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]

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The video synthetic aperture radar (VideoSAR) technology is widely used in military reconnaissance, geological exploration, and disaster prediction, among other fields. Owing to multiple interference factors in SAR videos, such as speckle noise, specular reflection, and overlay effect, moving targets are easily mixed with background or other targets. Therefore, this study proposed an effective VideoSAR target detection and tracking algorithm. Firstly, several features of VideoSAR were extracted to construct multichannel feature maps. Then, deeper features were extracted using the improved lightweight EfficientDet network, thus improving the accuracy of SAR target detection while considering algorithm efficiency. Finally, the trajectory association strategy based on bounding boxes was employed to associate the same target in VideoSAR. The experimental results show that the method proposed in this study is effective for SAR shadow target detection and tracking. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

The Axi mining area in Xinjiang has a complex geographical environment. The long - term exploitation of mineral resources has caused severe ground subsidence and deformation in the mining area, as well as safety hazards of mining and production and the destruction of the surrounding ecological environment. This study aims to further investigate and analyze the spatial - temporal variation characteristics of the ground subsidence and the patterns of surface deformation in the Axi mining area. To this end, this study first calculated the land subsidence using the small baseline subset - interferometric synthetic aperture Radar ( SB AS - InSAR) technique based on the 127 scenes descending Sentinel - 1A images acquired from February 9, 2017 to April 25, 2021. Then, it compared the subsidence monitoring results obtained using the InSAR technique with the leveling results for verification. Finally, this study analyzed the spatial - temporal variation characteristics of land subsidence in the Axi mining area in recent five years and investigated the driving factors for the land subsidence. The results show that the surface deformation of the Axi mining area showed a roughly stable trend and significant local subsidence throughout the monitoring period. The main factors affecting the ground subsidence included mineral exploitation, geological structure, precipitation, and the impoundment of open - pit mines. This study will provide a scientific basis for ground subsidence monitoring and the future proper exploitation of underground minerals in the Axi mining area. [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]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To meet the requirements of miniaturization, lightweight and precision of space optical instruments, an aperture driven by single-phase piezoelectric motor was designed. The whole structure of the apertures was designed to realize the integration of structure and function. Then, piezoelectric stators with high strength and power density were designed by using piezoelectric ceramics and carbon fiber composite materials. The dynamics characteristics of laminated piezoelectric stators were analyzed by COMSOL finite element software, and the modal frequency consistency was adjusted. The structure optimization and interference mode separation design of the moving rings of diaphragm were also completed. According to the design scheme, the prototype of piezoelectric apertures was manufactured. The mass is only 49 g, aperture range is in 0.5~35 mm.The experimental results show that the full opening and closing time of the apertures are as 89 ms and 92.4 ms respectively, and the minimum angular resolution is as 4x10-5 rad. This study simplifies the composition and control of the diaphragm, making it more compact in structures and smaller in weight while ensuring accuracy. [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]

In order to prevent the reconnaissance of synthetic aperture radar-ground moving target indication (SAR-GMTI) to moving targets on the ground, a smart jamming method of intermittent sampling based on noise multiplication modulation is proposed. Combining the intermittent sampling jamming with noise multiplication modulation, the jamming method can not only guarantee transceiver isolation, but also efficiently produce different effects of multiple target deception and dense target cover by the offline template design. The process performs intermittent sampling firstly, and then multiply product modulation with the designed template. The jamming signal model is established and the interference effect on SAR and SAR-GMTI is analyzed, the influence of reconnaissance error on performance is explained. The results show that the proposed method can make up for the defect of intermittent sampling effectively, whose distribution of false target is regular and distinguishable. The jamming scope is expanded and the jamming result is flexible. [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]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In view of the problem that both the antenna array and system complexity of large distributed synthetic microwave radiometer system increase with the improvement of imaging resolution, an imaging algorithm based on data fusion for synthetic aperture microwave radiometer with high-resolution is proposed. Firstly, the data fusion model is established. And then the data fusion algorithm is discussed in detail. Finally, a series of ground validation imaging tests are carried out and some imaging results are presented. Both the theoretical and experimental results demonstrate that the proposed algorithm is feasible and can effectively improve the imaging resolution. Moreover, it provides a new solution for enhancing the spatial resolution of microwave radiometric remote sensing from the perspective of data dimension. [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]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Energy Chemical Industry is the property of Energy Chemical Industry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Predicting the subsequent subsidence in mining areas according to the law of mining subsidence is the key to assessing mining risks and adjusting mining planning. This study determined the available conditions of the logistic model for mining subsidence prediction through analysis and simulation experiments and proposed a method for the dynamic prediction of mining subsidence based on small baseline subset % SBAS) - interferometric synthetic aperture radar %InSAR) technology and the logistic model. Firstly, the time - series subsidence data of a mining area was obtained using the SBAS - InSAR technology. Then, taking the time series subsidence data as the data for fitting, the parameters of the logistic model were calculated pixel by pixel by using the trust region algorithm. Then, the pixel range in which the subsequent subsidence can be predicted was determined according to the available conditions of the logistic model. Finally, according to the Logistic model, the subsequent subsidence within the predictable range was predicted. This method was applied to a certain mining area in Erdos City, Inner Mongolia for tests, and the prediction results were verified using the InSAR monitoring results of corresponding dates. The predicted results after 36 days and 108 days of ming had the root mean square error % RMSE) of 0.010 1 m and 0. 023 6 m, respectively, and their proportion with prediction errors of less than 0. 03 m reached 98. 9% and 89. 3%, respectively. These results indicate that the method for dynamic prediction proposed in this study has high prediction accuracy. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)