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Optical remote sensing imagery can serve as a crucial data source in numerous fields. However, the susceptibility to weather conditions (like clouds and rain) can render the cloud contamination, leading to the significant influence on optical images. The image quality can be required to restore the spectral information in cloud-contaminated regions. In this study, the spectral information was reconstructed in the cloud-contaminated regions using an object-oriented approach. Synthetic aperture radar (SAR) data was integrated with the radar signals and land cover information, in order to serve as the constraints for the reconstruction. The distance matrix was calculated on the SAR images, and then transplant onto the optical images for the replacement. The pixel-level matching accuracy was enhanced to maximize the retention of spectral information from the original images. A solid foundation was provided for the accurate extraction of subsequent land surface information. The performance was also evaluated to verify the improved model. The comparative experiments were conducted with the traditional pixel replacement, weighted linear regression, and curvature-driven. Visual inspection was carried out over various scenes. The results revealed that the superior quality was achieved in the imagery reconstruction in the thick cloud areas and cloud shadows. Particularly, the consistency was maintained among various land cover pixels in the reconstructed areas by the object-oriented method. The boundaries and areas were accurately delineated in the various types of fine-grained land cover. Quantitative analysis was conducted to validate using some metrics, such as correlation coefficient, root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). The spectral characteristic curves of the pixels were reconstructed to closely match those of the reference images, with the correlation coefficients exceeding 0.99. Notably, significant improvement was observed in the spectral curve recovery of vegetation pixels with the enhanced relative to the rest. The radar vegetation indices were optimized to employ an object-oriented matching mechanism during pixel reconstruction. The traditional pixel replacement model was optimized effectively. Furthermore, the band reflectance was closely resembled that of the reference images. Among the five bands of cloud-contaminated images after reconstruction, the highest correlation coefficients were observed with Band 2 and Band 3 reaching 0.925 and 0.922, respectively, and RMSE values of 0.009 and 0.007, respectively. The reconstructed images shared the minimal quality loss and high similarity to the reference images, with the highest PSNR and SSIM values across all bands. Specifically, the structural similarity values of the reconstructed Band 2 and Band 3 images were 0.902 and 0.910, respectively, with the PSNR values reaching 31.978 and 33.173, respectively. The superior image quality was obtained similar to reference images, which was consistence with spectral characteristics. The experimental results were used to the qualitatively and quantitatively validated the effectiveness of the reconstruction. The object-oriented approach can be expected to reconstruct the spectral information in the cloud-contaminated regions. The remotely sensed images were consistently produced with the higher spectral reconstruction accuracy in the different types of land cover and spectral bands. However, the replacing pixel spectral information was relied primarily on from single-image data. Some limitations was matched the effectiveness between cloudy and cloud-free regions, when reconstructing the large-scale, thick cloud areas in single remote sensing images. Additionally, the global searching can fail to the optimal reconstruction for the locally contaminated cloud pixels. Therefore, further investigation is required to determine the optimal local search range for the pixel matching using different extents of cloud contamination. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University 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.)

Aiming at the current situation of lack of environment and equipment in radar training, a radar operation training method based on the combination of virtual and real is proposed. Firstly, the connotation of the concept of virtual-real combination is analyzed. Secondly, the interaction methods of combining physical radar and virtual environment and virtual radar and physical environment are studied. Finally, combined with the specific training requirements, the application of real-real, real-virtual, virtual-real, and virtual-virtual radar interaction methods in radar training is analyzed in the same scenario. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

In order to effectively improve the capability of shore-to-ship missile penetration, unmanned aerial vehicles (UAV) were used for distributed combat against enemy shipborne radar. Based on radar detection equation and interference equation, the calculation model of radar exposure area under UAV interference was established, and the configuration of UAV array was analyzed. The results show that the jamming direction, jamming power and jamming distance of UAV have important effects on the detection performance of shipborne radar. The distributed operation of UAV can effectively reduce the combat effectiveness of radar, thus providing favorable conditions for shore-to-ship missile penetration, and laying an important theoretical foundation for future research on the tactical application of UAV as a new combat style. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University 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.)

Field roads have been the most important transportation ways for commercial grain and agricultural products in hilly and mountainous regions in China. Especially, the youth labor can rapidly migrate from the rural to the urban areas against everincreasing urbanization. Current manual production cannot fully meet the large-scale and precision agriculture in recent years, owing to the protracted yield cycle and low rate of return. Therefore, it is urgent to implement the mechanization and intelligent agriculture for the national food security in hilly and mountainous regions. Among them, the autonomous and safe operation of intelligent machinery can be critical to navigate or circumvent obstacles on field roads. However, the conventional machine vision cannot accurately and rapidly construct the visual guidance lines in such terrains. In this study, the extraction was proposed to enhance the visual guidance and obstacle avoidance using millimeter-wave radar and vision fusion. The state information was also detected from the target objects on the field road. The specific steps were as follow. Preprocessing techniques were employed to filter the portion of the radar object data. A multi-target tracking was utilized to eliminate the interference data for the continuous tracking of dynamic objects. Accurate radar object data was then obtained for subsequent data fusion. A semantic segmentation network was created using Deeplabv3+, and then leveraged a dataset of the adjacent field roads. The millimeter-wave radar and vision data were synchronized in both time and space via the timestamp alignment and least squares-based coordinate transformation. Extraction approach was then established for the visual guidance lines, particularly for the scenarios where dynamic target state was both available and unavailable. A series of experiments were carried out to validate the extraction of visual guide line. The average errors of detection were ranged from 1.60 to 9.20 pixels at the real road midpoints in the scenes without dynamic targets. Moreover, the visual guidance lines were successfully extracted for the obstacle avoidance proactively when encountering dynamic targets. Evidently, the integrated approach was effectively overcome the constraints of traditional machine vision, thereby enhancing the safety and reliability of machinery operation in hilly and mountainous terrains. Meanwhile, the efficacy was depended mainly on the precise projection of millimeter-wave radar data onto the visual plane. Simultaneously, the variables were markedly introduced the positional discrepancies, such as the posture of platform (including pitch and roll). Furthermore, the driveability of the operational area was effectively evaluated in the forthcoming regions. At the same time, the real-world conditions were more complex in the frequent presence of common elements like livestock, vehicles, and tricycles. The resilience of approach can be expected to improve the radar data processing for the detection of static objects. Moreover, positional deviations can also be mitigated from mechanical movements. As such, the precise visual guidance lines were established in the hilly and mountainous terrains. Therefore, the radar data processing can be focused on the dataset expanding and position deviation that induced by various factors. The intelligent level of agricultural machinery can be improved in hilly and mountainous area. The finding is of great significance to consolidate national food security. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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 Test & Measurement Technology is the property of Publishing Center of North University of China 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.)

Non-destructive testing of tree roots has the vast application potential to the root system evaluation of fruit and ancient trees for the better management of plant health. Among them, ground-penetrating radar (GPR) can be expected to nondestructively detect the tree roots, due to the portable and low-cost. However, the interpretation of GPR data is dependent mainly on the manual analysis, leading to the reduced automation and accuracy. Moreover, the clutters and noise that caused by soil heterogeneity can severely affect the accuracy of root detection and identification. In this study, a detection method was proposed for the clutter suppression and root parameter prediction of ground penetrating radar. The data collection was divided into two parts: the dataset and field test. The datasets consisted of simulated and synthesized real data. The roots with the radius ranging from 5 to 30 mm were randomly distributed within 300 mm underground. The field experiment was conducted in three sand pits. The soil heterogeneity was simulated to create the different moisture levels, when adding water into different parts in two of the dry sand pits. The rest sandpit was a condition of higher water content after heavy rainfall. At the same time, the roots were excavated to verify the model under real soil conditions. Four test pits contained the roots of different tree species, the different soil environments, as well as the different radii and depths. The attention mechanism was integrated into the U-net model, in order to enhance the capability in the clutter suppression and root target reflection restoration. A clutter suppression network was constructed to test the effectiveness of the model. The encoding-decoding structure of U-net was separated the target root hyperbolic reflection from the overall background. The attention mechanism enabled the network to better adaptively focus on the hyperbolic reflection of the target root system without identifying the clutter as the target reflection. The clutter suppression was realized for the original B-scan image to remove the adverse effects, due to the soil heterogeneity and radar antenna coupling. Furthermore, the network of root parameter prediction was constructed using residual blocks and inception. The multi-scale receptive field of inception was used to extract the global and local features, and simultaneously to predict the root radius and depth. The predicted results were obtained by parallel inputting the clutter suppressed image and the original B-scan image into the network. The effectiveness of the model was evaluated using datasets and field experiments. The test results of clutter suppression were evaluated using peak signal to noise ratio (PSNR) and structural similarity (SSIM), which were 39.42 dB and 0.991, respectively, better than before. The test results on datasets showed the mean absolute error (MAE) of 1.7 mm, and the coefficient of determination R² value of 0.914 for the root radius prediction, while the mean absolute error (MAE) of 6.3 mm and the R² value of 0.989 for the root depth prediction. The clutter suppression was also achieved in the better performance than the rest on the field data. The maximum prediction errors for the root radius and depth were 1.85, and 13.6 mm, respectively, where the total average relative error was 6.55%. Influencing factors were determined for the prediction effectiveness of ground-penetrating radar. The future research directions were pointed out as well. The findings can be applied to the actual prediction of root depth and radius of tree roots, particularly for the health administration of fruit trees and the protection of ancient trees.protection of ancient trees.protection of ancient trees. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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 Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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.)

In order to overcome the shortcomings of the traditional radar fault detection method, which is highly dependents on expert experience, consumes a lot of manpower and material resources, causes over-repair, cannot give advance warnings for degradation faults, and so on. An unsupervised radar degradation fault prediction method based on dynamic updated-neural network was proposed. Firstly, the historical data of peak power and operating frequency were collected by microwave measurement equipment. Secondly, the dynamic updated-neural network was used to dynamically update the historical data and predict the subsequent data. Finally, the isolated forest method was adopted for unsupervised fault detection on the predicted data. In this way, radar degradation fault prediction and early warning can be realized. The results show that the method proposed can predict degradation faults at least 10-time steps (100 minutes) in advance and give the real-time alarms. It also can realize radar degradation fault prediction when there are small samples, no fault samples, no feature extractions and no artificial thresholds. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Earth Science (1001-8166) is the property of Advances in Earth Science 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.)

All pineapples are harvested manually at present in China. But manual harvesting cannot fully meet the large-scale production against the ever-increasing greying workforce. Fortunately, automatic navigation can be expected to develop the pineapple harvester. In this study, a path planning algorithm was proposed as the navigation scheme to improve the mechanization and automation level of pineapple harvesting. An improved RRT* algorithm was also used for global path planning. Firstly, the self-heuristic idea was used to constrain the generation range of sampling points. Then, the bias probability pbias was introduced to generate the random sampling points. Specifically, the sampling points were randomly generated with the probability p in the space, when p>pbias. Otherwise, the target point was used as the sampling point, in order to decrease the blindness of sampling point generation. Thirdly, the gravitational field of the artificial potential field, and the concept of direction weight were introduced in the new node expansion. The weights wg and wk were assigned to the directions of the sampling and target point, respectively, where the direction was constrained in the expansion of the new node. Fourthly, the bidirectional expansion was used to speed up the iteration speed in the double-tree expansion. Finally, the greedy algorithm was applied to prune the redundant nodes of the path. The Cantmull-Rom interpolation function was also used to smooth the path corners. Three environments (including multiple obstacles, mazes, and narrow passages) were created to simulate the path planning process, in order to evaluate the performance among the improved navigation path planning, RRT* and bidirectional RRT* algorithm. Planning time, node number, and path length were selected as the indicators. Each algorithm experimented with 30 times in every single environment. The average, maximum, minimum, and standard deviation were calculated for the simulation data of the three indicators, respectively. The simulation results showed that the average planning time of the path planning algorithm in the three environments was 17.97%, and 46.12% higher than that of the RRT* and bidirectional RRT* algorithms, respectively, while the average programming speed was 4.7, and 2.2 times as rapid as that of the RRT*, and bidirectional RRT* algorithm, respectively. Furthermore, the average node number was 87.22% and 52.52% less than that of the RRT* and bidirectional RRT* algorithms, respectively. The average path length was 3.81% and 6.08% less than the RRT* and bidirectional RRT* algorithms, respectively. The field test showed that the planning time was only 14.12% and 20.34% of the RRT* and bidirectional RRT*, respectively. The iteration number was 80.89% and 69.70% less than that of the RRT* and bidirectional RRT*, respectively. In addition, the rotation angles larger than 60° on the path planned by RRT* and bidirectional RRT* algorithms were 1.56 and 2.06 times as much as that of the improved, respectively, and the rotation angles larger than 100° on the path were 1.55 and 2.18 times. The improved RRT* algorithm can fully meet the path navigation requirements of agricultural machinery in the field. The pineapple harvester can run along the planned path to the target point as moving with the speed of 0.2, 0.4, and 0.6 m/s, but the position and heading deviation increase with the moving speed. This finding can provide a sound reference for the navigation development in agricultural machines. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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 Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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 Aero Weaponry is the property of Aero Weaponry 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.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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 Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic 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.)

An accurate and rapid extraction of fruit tree canopy has been one of the most important steps to determine the precise application of pesticides in modern agriculture. However, the current detection cannot fully meet the harsh requirements in the complex environment of an orchard in recent years. In this study, a novel extraction system was proposed to identify the fruit tree canopy in an orchard using millimeter-wave radar. Firstly, the region of interest (ROI) was set to remove a large number of environmental point clouds, and then the random sampling was used to fit and segment the ground point clouds. Secondly, a density-based clustering was selected to integrate the resolution of millimeter-wave radar and the distance parameter of the point cloud. An adaptive density clustering was then established to deal with the different densities of the point cloud at different distances when using the millimeter-wave radar. As such, the neighborhood radius was adaptively adjusted in different directions, according to the distance from the point cloud to the radar. The point cloud density was then used to identify the fruit tree canopy, further improving the recognition performance of the point cloud. Finally, an Alpha-shape was selected to reconstruct the three-dimensional surface of the fruit tree. The optimal parameters of the three-dimensional reconstruction were achieved to evaluate the volume from Alpha values and the geometric measurement. The random sampling was also used to extract the structural parameters. The parameters of the mathematical model were estimated from a data set with a large number of outliers using multiple iterations. A curve model was then obtained closer to the actual situation. In addition, a field test was conducted to verify the feasibility of the model in the Loquat Plant Resource Garden (113.367 789°E, 23.164 129°N) of South China Agricultural University on April 2th, 2021. 40 yellow-bark fruit trees were also scanned in the test. The results showed that a higher performance was achieved to effectively identify and extract the point clouds of a single tree canopy, where the F1 score was 93.7% for the recognition accuracy of fruit trees. Furthermore, the average relative errors of the plant height and crown width in the fruit trees were 8.7% and 8.1%, respectively, while the coefficients of determination were 0.84 and 0.92, respectively, and the root mean square errors were 16.39 and 7.82, respectively, compared with the manual measurement. In addition, the average volume of fruit trees was 5.6 m³ using Alpha-shape, increasing by 59.4% in the accuracy of volume, compared with the traditional. Nevertheless, two recommendations can be addressed during this time: 1) To identify the fruit trees with the overlapping canopies using a relatively higher resolution of millimeter-wave radar; 2) To quantitatively extract the volume of the fruit tree in the harsh sense. Anyway, the millimeter-wave radar can be widely expected to accurately extract the canopy information in an orchard. The finding can provide a new promising technology to extract the canopy information for the data collection and automatic operation in modern agriculture. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing 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 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 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.)

Aiming at tlie problems of long period，low efficiency and poor versatility in radar rotating transmission combination system design， the method of modular design was put forward. The main advantages of tlie rotating transmission combination system based on modular design were analyzed. According to the radar technical requirements，the function modules of tlie rotating transmission combination system were de signed， and key technologies adopted in the process of tlie module design were expounded. The performance of rotating transmission combination system was tested. The results indicate that all the properties meet the rotating transmission requirements of radar for multi-media such as power， signal， optical， and liquid. [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 Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation 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 Nonferrous Metals (Mining Section) is the property of Beijing Research Institute of Mining & Metallurgy Technology Group 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 Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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 Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Satellite remote sensing has made great strides in the last few decades, which enables the long-term consistent observations of many variables of the terrestrial water cycle and thus advances the understanding of the terrestrial water cycle. This paper reviews the principles of remote sensing in retrieving key variables of the terrestrial water cycle, illustrates the progress of satellite remote sen-sing in hydrological applications, and discusses the future direction. Although most of hydrological states and fluxes variables are observable by remote sensing, closing terrestrial water budget with the remote sensing products is still an open question, suggesting more efforts are needed to improve the hydrological consistency of the remote sensing products. In the future, efforts should be made to develop new generation sensors and platforms for the consistent remote sensing products with finer spatiotemporal resolution. At the same time, efforts should be devoted to the evaluation of remote sen-sing products of the terrestrial water cycle through carrying out integrated field experiments. [ABSTRACT FROM AUTHOR]

A specific emitter identification( SEI) method based on deep reinforcement learning is proposed on account of the deficiency of emitter individual feature extraction depending on artificial experience. Due to the differences of the transient information of signal envelope,which results from the change of the signal owing to a nonideal transmitter channel,an envelope rising edge is used as the input state of deep neural network,and the emitter classifications are used as the optional actions of the current input state. The envelope features are extracted automatically through the convolutional neural network( CNN),and Q values of the current state action pairs are fitted,thus completing the specific emitter identification task based on the reinforcement learning model. The applications of deep Q network( DQN),deep double Q network( DDQN) and Dueling network in the specific emitter identification are discussed. The measured results show that the recognition rate of traditional machine learning algorithm is less than 80%,but the deep reinforcement learning model can achieve the high recognition rate of98. 42%. [ABSTRACT FROM AUTHOR]

To meet the requirements of accuracy and reliability of plant protection robot in ridge identification and route planning, also improve the working conditions of farmers, and achieve an unmanned operation purpose, an algorithm based on multi-support-vector proportioning weight of SVM (support vector machine) to identify the ridge line of vineyards, and the path planning of plant protection robots were proposed. The strategy first uses Kalman filter to pre-process coarse orchard data information obtained by Lidar scanning. According to the principle that Kalman filter complies with Gaussian distribution, the prior point between 2 adjacent points was taken as the prior state, and the latter point was used as the observed point to obtain posteriori state estimation, so as to realize data integration and analysis. With its good system state estimation characteristics, the collected data can be used to judge the trend of the ridge line, so as to correct the noise outliers in the data and improve the readability of the data. Then according to the characteristics of the vineyard branch ridge and the characteristics of ridges line with clearly separable spacing, and corresponding to the situation that the ridge line on both sides can be completely separated, the method was combined with SVM linear classification. With the unique advantages of the classification and due to that SVM can search the unique segmentation hyperplane, the maximum interval and segmentation hyperplane, classification margins in the ridge environment could be gotten. The split hyperplane obtained at this time would be between ridge lines. However, there was a big deviation from the angle of the direction of the ridge line and the horizontal distance. It could not meet the precise operation requirements of plant protection robots. It needed further data processing and analysis. In order to obtain accurate position of the center of ridge line, finally, the relative weights were assigned to the sample points of each ridge based on the geometric interval relationship between the sample points on both sides of the ridge and the corresponding SVM classification marginal line. The classification marginal line was reformed according to the number of sample points and the relative weights. According to the condition of the product of the interval relationship between each sample point and the classification margin, their relative weight must be consistent with the quality value of classification margin. The random sampling consistency iteration method (RANSAC) would avoid the error of cost estimate, and could estimate the parameters of the mathematical model from a group of observed data with outliers, so as to obtain the predicted safety location of the ridge. Although the pre-estimated security location of ridge line was not necessarily consistent with the actual location of the vineyard ridge, navigation line could be obtained indirectly by the security ridge line on both sides and the principle of angle bisector which could meet the requirements of precision operation of plant protection robot. Operation guidance line for plant protection robot could be acquired. After a number of actual samples were tested, the average angular deviation between the fitted navigation line and the actual ridge centerline was 0.72°, and the average distance deviation of the relative plant protection robot was 4.22 mm. Experimental results showed that this algorithm could effectively identify and locate the navigation route needed by the plant protection robot. The fitted navigation line could meet the requirements of accurate operation of the plant protection robot in the vineyard. However, the redundancy of the algorithm was relatively large, and the time required to process data in a single time was about 2.05 s. With the accelerated calculation speed of the processor in the future, the algorithm provided in the article can provide a reference solution for such a problem. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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 Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Traditional synthetic aperture radar (SAR) imaging relies on wideband transformed waveforms. SAR using narrowband continuous waveforms (NCW) has been proved to be also able to achieve high resolution imaging exploiting high Doppler resolution. NCW-SAR has the following advantages： it occupies very small band resources, it promotes the development of low-cost and small SAR, it is much applicable to passive SAR using illuminators of opportunity, which usually transmits waveforms with relatively narrow bandwidth. Firstly, the application potential of NCW-SAR is pointed out. The development of narrowband imaging is briefly reviewed and the state of art NCW-SAR imaging technology is analyzed. Then, a general SAR received signal model is presented and afterwards, a moving target imaging method for NCW-SAR is proposed to focus the moving targets on the ground scene. The velocity estimation method as well as the velocity resolution analysis is also presented. The simulation results demonstrate the effectiveness of the proposed NCW-SAR moving target imaging method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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 damage assessment method based on BN-Cloud model is put forward for the impact of complex and uncertain factors in modern battlefield environment. The characteristics of targets are analyzed,and a hierarchical evaluation index system is established,which is used to build a Bayesian network（ BN） structure. Monte Carlo method is introduced into parameter learning,a condition probability table（ CPT） of each network node is obtained through simulation,and the probability of each damage level is obtained by using the network inference. Finally,cloud model is used to transform target damage probability into real damage value so as to realize the transformation from uncertainty to certainty. The target physical damage degree is used as one of sub-nodes of functional damage degree in BN,in which CPT is used as a link. As a result,a new method to transform physical damage into function damage is realized. A radar target is taken as an example for simulation. The simulated result shows that the proposed method can be effectively used for target damage assessment,and achieves a significant improvement in accuracy and reliability compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

The expanding trend of soil salinization has become more and more severe especially in arid and semi-arid areas in the northwest China. Therefore it is specially important to dynamically monitor the soil salinization in the arid and semi-arid areas scientifically, accurately and rapidly. Microwave remote sensing technique has become a promising method to detect and monitor the soil salinity due to its many advantages. The aim of this study was to investigate the capability of C-band RADARSAT-2 SAR (synthetic aperture radar) data in soil salinity estimation over agricultural fields. In this study, Jiefangzha zone of Hetao irrigation district, Inner Mongolia, China was selected as the study area. Based on the back-scatter coefficient value and soil salt content, this paper used 3 kinds of methods including the multiple linear regression (MLR), geographically weighted regression (GWR) and back propagation artificial neural network (BP ANN) to establish the quantitative inversion models of soil salt content. Soil salinity information was extracted from the RADARSAT-2 SAR data, which had a kind of fine four-polarization SLC (single look complex) format and were bought in 2013, and covered an area of 25 km × 25 km with 8 m ground resolution. Taking the spatial unevenness distribution of the saline soil into account, 69 sampling points were designed in the study area, and field digging depth of soil was 10 cm. Hand-held GPS (global position system) receiver was used to record the coordinates of sampling points, and the soil total soluble salt content was measured in the indoor. Mainly use the SAR Scape module of ENVI software to perform the radar image processing, including radiometric calibration, geometric correction, slant range turning and filtering. The four-polarization back-scatter coefficient values corresponding to the sampling points were extracted based on the previous results by the spatial analysis module of ArcGIS. Total salt content was took as dependent variable, four-polarization back-scatter coefficient values and soil moisture as independent variables, and the MLR and GWR salinity prediction models were established. Use the MLR module of SPSS and the GWR module of GWR4 software to deal with data respectively. The correlation of GWR model was higher than the MLR, but these 2 statistical models were not significant, and the inversion results were difficult to reflect the distribution of severe salinity and saline-alkali soil. Therefore the emphasis was focused on the building of the 8:140:1 structure of three-layer BP ANN model. Training set contained the data of 49 sampling points in the test area, the input layer was made up of sampling point coordinates, soil moisture, four-polarization back-scattering coefficients and their combined value, and the number of net neurons was 8; the output layer was a neuron corresponding to the total salt content of sample point. After a lot of tentatively computation, the optimal number of neurons in the hidden layer was selected as 140. The hidden layer of the three-layer BP ANN model used the hyperbolic tangent sigmoid activation function, the output layer utilized the linear activation function, the traingda function was selected to perform network learning and training, and the fitting target error was set as 0.1 mg/g. Then the BP model was applied to the RADARSAT-2 remote sensing images to achieve the high-precision quantitative inversion of soil salinity when the BP model inversion accuracy met the requirements of salinity prediction. Whole calculation process was implemented by the MATLAB neural network toolbox. It was found that the correlation of the MLR and GWR models was weak and their standard error (SE) was 0.55 and 0.47 mg/g respectively, but the SE of internal and external inspection using the ANN (BP) model was only 0.24 and 0.33 mg/g respectively, better than the other 2 models, whose salinity area accounted for about 65. 4%, and was basically consistent with ground validation. The artificial intelligence inversion model of soil salt considered the moisture’s influence, was directly based on the polarization radar scattering coefficients and their composition, and had no complex dielectric constant model. So the ANN (BP) model can reduce the smoothing effect compared with the 2 traditional models and improve the accuracy and reliability of model predictions, which meets the needs of soil salinity monitoring to a certain extent, and can promote and develop the application of microwave remote sensing in the soil salinity monitoring. [ABSTRACT FROM AUTHOR]

Facing with some potential problems of external damage on the transmission lines such as cranes and theft ,an on-line monitoring system against external damage based on the radar and video is put forward .According to the working principle of radar sensor ,the methods of measuring velocity and distance and detection angle, the back-end hardware circuit, software flow and the installation angle are designed. Then taking the classical spectrum estimation method based on the fast Fourier transform as the core algorithm, the speed of operation is greatly improved. In addition, the monitoring system has realized the calculation of detection distance, speed and other information and the detection of the objects that invade the surrounding area of the transmission lines. By building the experiment platform, compared with actual data, the measured data is almost consistent with the actual data, which verifies the reliability and practicability of the device. [ABSTRACT FROM AUTHOR]

Signal echoes from radar targets are usually buried in thermal noise and clutter, reflected back to the receiver by buildings, clouds, and the sea. The clutter-plus-noise power is not known at any given location, thus a fixed-threshold detection scheme cannot be applied if the false alarm rate is to be controlled. A class of schemes that can be used to overcome the problem of clutter; the constant false alarm rate (CFAR) processing schemes, set a threshold adaptively based on local information of total noise power. CFAR detection of radar targets can be implemented by a sliding-window detection approach or through the clutter map technique. The clutter map technique is often used for the radar target detection in a non-homogeneous background where the clutter power in spatial domain changes abruptly. However, the detection performance of the classic Nitzberg's clutter map on a non-Gaussian distributed background with a heavy long-tail greatly degrades. In our work, a new clutter map detection method based on non-coherent integration is presented. The analytic results show that its detection performance improves in a more spiky clutter environment, and it can be easily implemented. [ABSTRACT FROM AUTHOR]