Vessel sailing route extraction and analysis from satellite-based AIS data using density clustering and probability algorithms

Chen, Jin; Chen, Hao; Chen, Quan; Song, Xin; Wang, Hongdong

doi:10.1016/j.oceaneng.2023.114627

Cited by 7 publications

(2 citation statements)

References 41 publications

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“…By choosing clusters with large densities (using the quantile or KDE method), the main sailing routes of the analyzed region can finally be acquired. The cluster number of the comparative clustering methods was chosen following the practice in [47].…”

Section: Performance Comparison Experimentsmentioning

confidence: 99%

A Method for Clustering and Analyzing Vessel Sailing Routes Efficiently from AIS Data Using Traffic Density Images

Mou,

Fan,

et al. 2023

JMSE

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A vessel automatic identification system (AIS) provides a large amount of dynamic vessel information over a large coverage area and data volume. The AIS data are a typical type of big geo-data with high dimensionality, large noise, heterogeneous densities, and complex distributions. This poses a challenge for the clustering and analysis of vessel sailing routes. This study proposes an efficient vessel sailing route clustering and analysis method based on AIS data that uses traffic density images to transform the clustering problem of complex AIS trajectories into an image processing problem. First, a traffic density image is constructed based on the statistics of the preprocessed AIS data. Next, the main sea route regions of traffic density images are extracted based on local image features, geometric structures, and spatial features. Finally, the sailing trajectories are clustered using the extracted sailing patterns. Based on actual vessel AIS data, multimethod comparisons and performance analysis experiments are conducted to verify the feasibility and effectiveness of the proposed method. These experimental results reveal that the proposed method displays potential for the clustering task of challenging vessel sailing routes.

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Section: Performance Comparison Experimentsmentioning

confidence: 99%

A Method for Clustering and Analyzing Vessel Sailing Routes Efficiently from AIS Data Using Traffic Density Images

Mou,

Fan,

et al. 2023

JMSE

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“…FitzGerald et al [ 7 ] pointed out that the Euler deconvolution yields many similar or duplicate solutions, which may be tightly clustered in the vicinity of real sources, i.e., the 14th strategy in the paper [ 7 ]. Based on this strategy, Cao et al [ 21 ] proposed a non-parametric estimation method based on the normalized B-spline probability density (BSS), aiming to separate the Euler solution clusters to mark different anomaly sources according to the similarity and density characteristics of the Euler solutions [ 22 , 23 ]. B-spline curves are linear combinations of B-spline basis functions connected by node vectors [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

FFT-Based Probability Density Imaging of Euler Solutions

Cao,

Chen,

et al. 2024

Entropy

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When using traditional Euler deconvolution optimization strategies, it is difficult to distinguish between anomalies and their corresponding Euler tails (those solutions are often distributed outside the anomaly source, forming “tail”-shaped spurious solutions, i.e., misplaced Euler solutions, which must be removed or marked) with only the structural index. The nonparametric estimation method based on the normalized B-spline probability density (BSS) is used to separate the Euler solution clusters and mark different anomaly sources according to the similarity and density characteristics of the Euler solutions. For display purposes, the BSS needs to map the samples onto the estimation grid at the points where density will be estimated in order to obtain the probability density distribution. However, if the size of the samples or the estimation grid is too large, this process can lead to high levels of memory consumption and excessive computation times. To address this issue, a fast linear binning approximation algorithm is introduced in the BSS to speed up the computation process and save time. Subsequently, the sample data are quickly projected onto the estimation grid to facilitate the discrete convolution between the grid and the density function using a fast Fourier transform. A method involving multivariate B-spline probability density estimation based on the FFT (BSSFFT), in conjunction with fast linear binning appropriation, is proposed in this paper. The results of two random normal distributions show the correctness of the BSS and BSSFFT algorithms, which is verified via a comparison with the true probability density function (pdf) and Gaussian kernel smoothing estimation algorithms. Then, the Euler solutions of the two synthetic models are analyzed using the BSS and BSSFFT algorithms. The results are consistent with their theoretical values, which verify their correctness regarding Euler solutions. Finally, the BSSFFT is applied to Bishop 5X data, and the numerical results show that the comprehensive analysis of the 3D probability density distributions using the BSSFFT algorithm, derived from the Euler solution subset of x0,y0,z0, can effectively separate and locate adjacent anomaly sources, demonstrating strong adaptability.

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Smart3DMOT: Smart cascade 3D MOT tracking strategy with motion and appearance association

Hong,

Zhou,

2024

Computers and Electrical Engineering

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Vessel sailing route extraction and analysis from satellite-based AIS data using density clustering and probability algorithms

Cited by 7 publications

References 41 publications

A Method for Clustering and Analyzing Vessel Sailing Routes Efficiently from AIS Data Using Traffic Density Images

A Method for Clustering and Analyzing Vessel Sailing Routes Efficiently from AIS Data Using Traffic Density Images

FFT-Based Probability Density Imaging of Euler Solutions

Smart3DMOT: Smart cascade 3D MOT tracking strategy with motion and appearance association

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