A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
To improve the feature extraction method for ship trajectories and enhance trajectory classification performance, this paper proposes a ship trajectory classification model that combines a one-dimensional residual network (ResNet1D) and an attention-based Long short-term memory network (AttLSTM). Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/7/3489 |
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| author | Jiankang Ke Faxing Lu Yifei Liu Bing Fu |
| author_facet | Jiankang Ke Faxing Lu Yifei Liu Bing Fu |
| author_sort | Jiankang Ke |
| collection | DOAJ |
| description | To improve the feature extraction method for ship trajectories and enhance trajectory classification performance, this paper proposes a ship trajectory classification model that combines a one-dimensional residual network (ResNet1D) and an attention-based Long short-term memory network (AttLSTM). The model aims to address the limitations of traditional methods in extracting feature patterns jointly represented by non-adjacent local regions in ship trajectories, optimized through the introduction of a self-attention mechanism. Specifically, the model first utilizes the ResNet1D module to progressively extract implicit motion pattern features from local to global levels, while the AttLSTM module captures temporal sequence features of ship trajectories. Finally, the fusion of these two types of features generates a more comprehensive and rich spatiotemporal motion feature representation, enabling accurate classification of five types of ship trajectories, including towing vessels, fishing vessels, sailing vessels, passenger ships, and tankers. Experimental results show that this model excels on extensive real-world trajectory datasets, achieving a classification accuracy of 89.7%, significantly outperforming models relying solely on single feature sets or lacking integrated attention mechanisms. This not only validates the model’s superior performance in ship trajectory classification tasks but also demonstrates its potential and effectiveness for practical applications. |
| format | Article |
| id | doaj-art-476f15f227704f33bdb0eed4b861e2dc |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-476f15f227704f33bdb0eed4b861e2dc2025-08-20T02:17:00ZengMDPI AGApplied Sciences2076-34172025-03-01157348910.3390/app15073489A ResNet1D-AttLSTM-Based Approach for Ship Trajectory ClassificationJiankang Ke0Faxing Lu1Yifei Liu2Bing Fu3Naval University of Engineering, Wuhan 430033, ChinaNaval University of Engineering, Wuhan 430033, ChinaNaval University of Engineering, Wuhan 430033, ChinaNaval University of Engineering, Wuhan 430033, ChinaTo improve the feature extraction method for ship trajectories and enhance trajectory classification performance, this paper proposes a ship trajectory classification model that combines a one-dimensional residual network (ResNet1D) and an attention-based Long short-term memory network (AttLSTM). The model aims to address the limitations of traditional methods in extracting feature patterns jointly represented by non-adjacent local regions in ship trajectories, optimized through the introduction of a self-attention mechanism. Specifically, the model first utilizes the ResNet1D module to progressively extract implicit motion pattern features from local to global levels, while the AttLSTM module captures temporal sequence features of ship trajectories. Finally, the fusion of these two types of features generates a more comprehensive and rich spatiotemporal motion feature representation, enabling accurate classification of five types of ship trajectories, including towing vessels, fishing vessels, sailing vessels, passenger ships, and tankers. Experimental results show that this model excels on extensive real-world trajectory datasets, achieving a classification accuracy of 89.7%, significantly outperforming models relying solely on single feature sets or lacking integrated attention mechanisms. This not only validates the model’s superior performance in ship trajectory classification tasks but also demonstrates its potential and effectiveness for practical applications.https://www.mdpi.com/2076-3417/15/7/3489ship trajectory classificationresidual neural network (ResNet)long short-term memory network (LSTM)self-attention mechanism |
| spellingShingle | Jiankang Ke Faxing Lu Yifei Liu Bing Fu A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification Applied Sciences ship trajectory classification residual neural network (ResNet) long short-term memory network (LSTM) self-attention mechanism |
| title | A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification |
| title_full | A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification |
| title_fullStr | A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification |
| title_full_unstemmed | A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification |
| title_short | A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification |
| title_sort | resnet1d attlstm based approach for ship trajectory classification |
| topic | ship trajectory classification residual neural network (ResNet) long short-term memory network (LSTM) self-attention mechanism |
| url | https://www.mdpi.com/2076-3417/15/7/3489 |
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