A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data

AIS (Automatic Identification System) is an effective navigation aid system aimed to realize ship monitoring and collision avoidance. Space-based AIS data, which are received by satellites, have become a popular and promising approach for providing ship information around the world. To recognize the...

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Autores principales:	Yitao Wang, Lei Yang, Xin Song, Quan Chen, Zhenguo Yan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	space-based AIS ship classification integrated learning data mining Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/c008137e80f5448da4ebbed0e0533e3e
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spelling	oai:doaj.org-article:c008137e80f5448da4ebbed0e0533e3e2021-11-11T15:21:55ZA Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data10.3390/app1121103362076-3417https://doaj.org/article/c008137e80f5448da4ebbed0e0533e3e2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10336https://doaj.org/toc/2076-3417AIS (Automatic Identification System) is an effective navigation aid system aimed to realize ship monitoring and collision avoidance. Space-based AIS data, which are received by satellites, have become a popular and promising approach for providing ship information around the world. To recognize the types of ships from the massive space-based AIS data, we propose a multi-feature ensemble learning classification model (MFELCM). The method consists of three steps. Firstly, the static and dynamic information of the original data is preprocessed and features are then extracted in order to obtain static feature samples, dynamic feature distribution samples, time-series samples, and time-series feature samples. Secondly, four base classifiers, namely Random Forest, 1D-CNN (one-dimensional convolutional neural network), Bi-GRU (bidirectional gated recurrent unit), and XGBoost (extreme gradient boosting), are trained by the above four types of samples, respectively. Finally, the base classifiers are integrated by another Random Forest, and the final ship classification is outputted. In this paper, we use the global space-based AIS data of passenger ships, cargo ships, fishing boats, and tankers. The model gets a total accuracy of 0.9010 and an F1 score of 0.9019. The experiments prove that MFELCM is better than the base classifiers. In addition, MFELCM can achieve near real-time online classification, which has important applications in ship behavior anomaly detection and maritime supervision.Yitao WangLei YangXin SongQuan ChenZhenguo YanMDPI AGarticlespace-based AISship classificationintegrated learningdata miningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10336, p 10336 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	space-based AIS ship classification integrated learning data mining Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	space-based AIS ship classification integrated learning data mining Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yitao Wang Lei Yang Xin Song Quan Chen Zhenguo Yan A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data
description	AIS (Automatic Identification System) is an effective navigation aid system aimed to realize ship monitoring and collision avoidance. Space-based AIS data, which are received by satellites, have become a popular and promising approach for providing ship information around the world. To recognize the types of ships from the massive space-based AIS data, we propose a multi-feature ensemble learning classification model (MFELCM). The method consists of three steps. Firstly, the static and dynamic information of the original data is preprocessed and features are then extracted in order to obtain static feature samples, dynamic feature distribution samples, time-series samples, and time-series feature samples. Secondly, four base classifiers, namely Random Forest, 1D-CNN (one-dimensional convolutional neural network), Bi-GRU (bidirectional gated recurrent unit), and XGBoost (extreme gradient boosting), are trained by the above four types of samples, respectively. Finally, the base classifiers are integrated by another Random Forest, and the final ship classification is outputted. In this paper, we use the global space-based AIS data of passenger ships, cargo ships, fishing boats, and tankers. The model gets a total accuracy of 0.9010 and an F1 score of 0.9019. The experiments prove that MFELCM is better than the base classifiers. In addition, MFELCM can achieve near real-time online classification, which has important applications in ship behavior anomaly detection and maritime supervision.
format	article
author	Yitao Wang Lei Yang Xin Song Quan Chen Zhenguo Yan
author_facet	Yitao Wang Lei Yang Xin Song Quan Chen Zhenguo Yan
author_sort	Yitao Wang
title	A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data
title_short	A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data
title_full	A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data
title_fullStr	A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data
title_full_unstemmed	A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data
title_sort	multi-feature ensemble learning classification method for ship classification with space-based ais data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c008137e80f5448da4ebbed0e0533e3e
work_keys_str_mv	AT yitaowang amultifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata AT leiyang amultifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata AT xinsong amultifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata AT quanchen amultifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata AT zhenguoyan amultifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata AT yitaowang multifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata AT leiyang multifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata AT xinsong multifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata AT quanchen multifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata AT zhenguoyan multifeatureensemblelearningclassificationmethodforshipclassificationwithspacebasedaisdata
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A Multi-Feature Ensemble Learning Classification Method for Ship Classification with Space-Based AIS Data

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