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Metaphor are commonly used rhetorical devices in linguistics. Among the various types, spatial metaphors are relatively common because of their intuitive and sensible nature. There are also many studies that use spatial metaphors to express non-location data in the field of visualization. For instance, some virtual terrains can be built based on computer technologies and visualization methods. In virtual terrains, the original abstract data can obtain specific positions, shapes, colors, etc. and people’s visual and image thinking can play a role. In addition, the theories and methods used in the space field could be applied to help people observe and analyze abstract data. However, current research has limited the use of these space theories and methods. For instance, many existing map theories and methods are not well combined. In addition, it is difficult to fully display data in virtual terrains, such as showing the structure and relationship at the same time. Facing the above problems, this study takes hierarchical data as the research object and expresses both the data structure and relationship from a spatial perspective. First, the conversion from high-dimensional non-location data to two-dimensional discrete points is achieved by a dimensionality reduction algorithm to reflect the data relationship. Based on this, kernel density estimation interpolation and fractal noise algorithms are used to construct terrain features in the virtual terrains. Under the control of the kernel density search radius and noise proportion, a multi-scale terrain model is built with the help of level of detail (LOD) technology to express the hierarchical structure and support the multi-scale analysis of data. Finally, experiments with actual data are carried out to verify the proposed method.
Rui Xin; Tinghua Ai; Ruoxin Zhu; Bo Ai; Min Yang; Liqiu Meng. A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data. ISPRS International Journal of Geo-Information 2021, 10, 379 .
AMA StyleRui Xin, Tinghua Ai, Ruoxin Zhu, Bo Ai, Min Yang, Liqiu Meng. A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data. ISPRS International Journal of Geo-Information. 2021; 10 (6):379.
Chicago/Turabian StyleRui Xin; Tinghua Ai; Ruoxin Zhu; Bo Ai; Min Yang; Liqiu Meng. 2021. "A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data." ISPRS International Journal of Geo-Information 10, no. 6: 379.
Very high resolution (VHR) satellite and aerial images often suffer from scene occlusion caused by redundant objects. The task of removing these redundant objects can be solved by missing data reconstruction technology. However, when dealing with VHR images with large-scale missing regions, existing spatial-based methods often destroy the structural information of ground objects. To alleviate this problem, this paper proposes a novel missing data reconstruction method based on deep learning. The reconstruction process is divided into two parts: structure prediction and texture generation. First, a progressive edge generation network (PEGN) is designed to predict the edges of objects in missing regions in a progressive manner. Then, the edge map predicted by PEGN is input to a texture generation network (TGN) as structural information to produce the reconstruction results. This is a spatial-based method that can produce realistic and reasonable results without any need for auxiliary spectral or temporal data. Experiments demonstrate that our model can better restore the structure of ground objects in VHR images than other spatial-based methods and outperform them in SSIM and PSNR indices. In addition, our model also has a strong generalization capability by introducing Poisson blending and histogram matching.
Hanwen Xu; Xinming Tang; Bo Ai; XiaoMing Gao; Fanlin Yang; Zhen Wen. Missing data reconstruction in VHR images based on progressive structure prediction and texture generation. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 171, 266 -277.
AMA StyleHanwen Xu, Xinming Tang, Bo Ai, XiaoMing Gao, Fanlin Yang, Zhen Wen. Missing data reconstruction in VHR images based on progressive structure prediction and texture generation. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 171 ():266-277.
Chicago/Turabian StyleHanwen Xu; Xinming Tang; Bo Ai; XiaoMing Gao; Fanlin Yang; Zhen Wen. 2020. "Missing data reconstruction in VHR images based on progressive structure prediction and texture generation." ISPRS Journal of Photogrammetry and Remote Sensing 171, no. : 266-277.
There is a large error in the actual radar trajectory tracking process. Track initiation is the primary problem in trajectory tracking and the first step in target tracking. The current track initiation algorithms are greatly affected by heavy clutter environments, so it is necessary to propose an algorithm to solve the problem of low track initiation efficiency. This paper presents a track initiation algorithm using a residual threshold in heavy clutter environments. The falling probability of measured value and decision threshold are used to determine the correlation window. The angle limiting condition is added to establish the track association, and the residual threshold is used to further eliminate the false tracks. The initial track experiment with the trajectory data in the sea near Rizhao Port shows that the algorithm is superior to the traditional logic method and Kalman filter method in track quality. The experiment uses the AIS buffer zone to calculate track initiation probability and uses the multi-region AIS trajectory data for verification. The experimental result shows that track initiation probability with the proposed algorithm in this paper can reach 92.31%.
Yaru Zhang; Aichao Liu; Chao Liu; Bo Ai; Xiao Zhang. A Track Initiation Algorithm Using Residual Threshold for Shore-Based Radar in Heavy Clutter Environments. Journal of Marine Science and Engineering 2020, 8, 614 .
AMA StyleYaru Zhang, Aichao Liu, Chao Liu, Bo Ai, Xiao Zhang. A Track Initiation Algorithm Using Residual Threshold for Shore-Based Radar in Heavy Clutter Environments. Journal of Marine Science and Engineering. 2020; 8 (8):614.
Chicago/Turabian StyleYaru Zhang; Aichao Liu; Chao Liu; Bo Ai; Xiao Zhang. 2020. "A Track Initiation Algorithm Using Residual Threshold for Shore-Based Radar in Heavy Clutter Environments." Journal of Marine Science and Engineering 8, no. 8: 614.
When it comes to feature retention in multi-scale representations of ocean flow fields, not all data points are equal. Therefore, this paper proposes a method of selecting data points based on their importance. First, an autocorrelation analysis is performed on flow speed and the rate of change in flow direction. Then, the magnitude of speed and variation in the rate of change in flow direction are classified. Feature regions are determined according to autocorrelation aggregation and classification analysis. Then, rough set theory and evidence theory are applied, using these results to determine the weights of different points. Finally, these weights are used to construct multi-scale representations of ocean flow fields, which effectively retain flow-field characteristics.
Bo Ai; Decheng Sun; Yanmei Liu; Chengming Li; Fanlin Yang; Yong Yin; Huibo Tian. Multi-Scale Representation of Ocean Flow Fields Based on Feature Analysis. ISPRS International Journal of Geo-Information 2020, 9, 307 .
AMA StyleBo Ai, Decheng Sun, Yanmei Liu, Chengming Li, Fanlin Yang, Yong Yin, Huibo Tian. Multi-Scale Representation of Ocean Flow Fields Based on Feature Analysis. ISPRS International Journal of Geo-Information. 2020; 9 (5):307.
Chicago/Turabian StyleBo Ai; Decheng Sun; Yanmei Liu; Chengming Li; Fanlin Yang; Yong Yin; Huibo Tian. 2020. "Multi-Scale Representation of Ocean Flow Fields Based on Feature Analysis." ISPRS International Journal of Geo-Information 9, no. 5: 307.
Triangulated irregular networks (TINs) are widely used in terrain visualization due to their accuracy and efficiency. However, the conventional algorithm for multi-scale terrain rendering, based on TIN, has many problems, such as data redundancy and discontinuities in scale transition. To solve these issues, a method based on a detail-increment model for the construction of a continuous-scale hierarchical terrain model is proposed. First, using the algorithm of edge collapse, based on a quadric error metric (QEM), a complex terrain base model is processed to a most simplified model version. Edge collapse records at different scales are stored as compressed incremental information in order to make the rendering as simple as possible. Then, the detail-increment hierarchical terrain model is built using the incremental information and the most simplified model version. Finally, the square root of the mean minimum quadric error (MMQE), calculated by the points at each scale, is considered the smallest visible object (SVO) threshold that allows for the scale transition with the required scale or the visual range. A point cloud from Yanzhi island is converted into a hierarchical TIN model to verify the effectiveness of the proposed method. The results show that the method has low data redundancy, and no error existed in the topology. It can therefore meet the basic requirements of hierarchical visualization.
Bo Ai; Linyun Wang; Fanlin Yang; Xianhai Bu; Yaoyao Lin; Guannan Lv; Ai; Wang; Yang; Bu; Lin; Lv. Continuous-Scale 3D Terrain Visualization Based on a Detail-Increment Model. ISPRS International Journal of Geo-Information 2019, 8, 465 .
AMA StyleBo Ai, Linyun Wang, Fanlin Yang, Xianhai Bu, Yaoyao Lin, Guannan Lv, Ai, Wang, Yang, Bu, Lin, Lv. Continuous-Scale 3D Terrain Visualization Based on a Detail-Increment Model. ISPRS International Journal of Geo-Information. 2019; 8 (10):465.
Chicago/Turabian StyleBo Ai; Linyun Wang; Fanlin Yang; Xianhai Bu; Yaoyao Lin; Guannan Lv; Ai; Wang; Yang; Bu; Lin; Lv. 2019. "Continuous-Scale 3D Terrain Visualization Based on a Detail-Increment Model." ISPRS International Journal of Geo-Information 8, no. 10: 465.
Traditional static flow field visualization methods suffer from many problems, such as a lack of continuity expression in the vector field, uneven distribution of seed points, messy visualization, and time-consuming calculations. In response to these problems, this paper proposes a multi-scale mapping method based on real-time feature streamlines. The method uses feature streamlines to solve the problem of continuity expression in flow fields and introduces a streamline tracking algorithm which combines adaptive step length with velocity matching to render feature streamlines in a real-time and multi-scale way. In order to improve the stability and uniformity of the seed point layout, this method uses two different point placement methods which utilize a global regular grid distribution algorithm and feature area random distribution algorithm. In addition, this method uses a collision detection algorithm to detect and deal with the unreasonable covering between streamlines, which alleviates visual confusion in the resulting drawing. This method also uses HTML5 Canvas to render streamlines, which greatly improves the drawing speed. Therefore, use of this method can not only improve the uniformity of the seed point layout and the speed of drawing but also solve the problems of continuity expression in the vector field and messy visualization.
Yu Fang; Bo Ai; Jing Fang; Wenpeng Xin; Xiangwei Zhao; Guannan Lv. Multi-Scale Flow Field Mapping Method Based on Real-Time Feature Streamlines. ISPRS International Journal of Geo-Information 2019, 8, 335 .
AMA StyleYu Fang, Bo Ai, Jing Fang, Wenpeng Xin, Xiangwei Zhao, Guannan Lv. Multi-Scale Flow Field Mapping Method Based on Real-Time Feature Streamlines. ISPRS International Journal of Geo-Information. 2019; 8 (8):335.
Chicago/Turabian StyleYu Fang; Bo Ai; Jing Fang; Wenpeng Xin; Xiangwei Zhao; Guannan Lv. 2019. "Multi-Scale Flow Field Mapping Method Based on Real-Time Feature Streamlines." ISPRS International Journal of Geo-Information 8, no. 8: 335.
The traditional sea surface temperature (SST) inversion model has a complicated parameter fitting process and poor adaptability in different sea areas. This paper presents an infrared remote sensing inversion model of SST based on deep neural network to refine the situation. The training data are the moderate-resolution imaging spectroradiometer (MODIS) infrared remote sensing data on sunny days and measured data from buoy in Bohai. The accuracy of inversion results is analyzed, the determination coefficient of inversion and measured values is 0.98, the standard error is 0.71°C and the mean absolute deviation is 0.85°C, the results show good accuracy of the model. The accuracy of Bohai SST inversion results is compared with SST products from the MODIS sensors and the inversion model is applied to other sea areas, demonstrating the credibility and portability of the model. The data experiments in this paper prove the feasibility of the model, which provides ideas for global SST inversion.
Bo Ai; Zhen Wen; Yingchao Jiang; Song Gao; Guannan Lv. Sea surface temperature inversion model for infrared remote sensing images based on deep neural network. Infrared Physics & Technology 2019, 99, 231 -239.
AMA StyleBo Ai, Zhen Wen, Yingchao Jiang, Song Gao, Guannan Lv. Sea surface temperature inversion model for infrared remote sensing images based on deep neural network. Infrared Physics & Technology. 2019; 99 ():231-239.
Chicago/Turabian StyleBo Ai; Zhen Wen; Yingchao Jiang; Song Gao; Guannan Lv. 2019. "Sea surface temperature inversion model for infrared remote sensing images based on deep neural network." Infrared Physics & Technology 99, no. : 231-239.
With the development of the maritime economy, sea traffic is becoming more and more crowded, and sea accidents are also increasing. Research on maritime search and rescue decision-making technology cannot be delayed. This paper studies the maritime search and rescue decision algorithm, based on the optimal search theory. It also analyzes three important concepts: Probability of containment (POC), probability of detection (POD), and probability of success (POS) involved in the maritime search and rescue decision-making process. In this paper, the calculation methods of POC and POD variables have been improved, and the search success rate has been improved to some extent. Finally, an example analysis of the maritime search and rescue incident is given. Through verification, the algorithm proposed in this paper can support maritime search and rescue decisions.
Donatien Agbissoh Otote; Benshuai Li; Bo Ai; Song Gao; Jing Xu; Xiaoying Chen; Guannan Lv. A Decision-Making Algorithm for Maritime Search and Rescue Plan. Sustainability 2019, 11, 2084 .
AMA StyleDonatien Agbissoh Otote, Benshuai Li, Bo Ai, Song Gao, Jing Xu, Xiaoying Chen, Guannan Lv. A Decision-Making Algorithm for Maritime Search and Rescue Plan. Sustainability. 2019; 11 (7):2084.
Chicago/Turabian StyleDonatien Agbissoh Otote; Benshuai Li; Bo Ai; Song Gao; Jing Xu; Xiaoying Chen; Guannan Lv. 2019. "A Decision-Making Algorithm for Maritime Search and Rescue Plan." Sustainability 11, no. 7: 2084.