Browse > Article
http://dx.doi.org/10.7780/kjrs.2022.38.4.10

Random Forest Classifier-based Ship Type Prediction with Limited Ship Information of AIS and V-Pass  

Jeon, Ho-Kun (Major of Ocean and Coastal Engineering, University of Science and Technology)
Han, Jae Rim (Marine Bigdata Center, Korea Institute of Ocean Science and Technology)
Publication Information
Korean Journal of Remote Sensing / v.38, no.4, 2022 , pp. 435-446 More about this Journal
Abstract
Identifying ship types is an important process to prevent illegal activities on territorial waters and assess marine traffic of Vessel Traffic Services Officer (VTSO). However, the Terrestrial Automatic Identification System (T-AIS) collected at the ground station has over 50% of vessels that do not contain the ship type information. Therefore, this study proposes a method of identifying ship types through the Random Forest Classifier (RFC) from dynamic and static data of AIS and V-Pass for one year and the Ulsan waters. With the hypothesis that six features, the speed, course, length, breadth, time, and location, enable to estimate of the ship type, four classification models were generated depending on length or breadth information since 81.9% of ships fully contain the two information. The accuracy were average 96.4% and 77.4% in the presence and absence of size information. The result shows that the proposed method is adaptable to identifying ship types.
Keywords
Ship type; Classification; Random forest; Decision tree; Machine learning; AIS; V-Pass;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
연도 인용수 순위
1 Pappas, O. and A. Achim, 2018. Superpixel-Level CFAR Detectors for Ship Detection in SAR Imagery, IEEE Geoscience and Remote Sensing Letters, 15(9): 1397-1401. https://doi.org/10.1109/LGRS.2018.2838263   DOI
2 Wessel, P. and W.H.F. Smith, 1996. A global, self-consistent, hierarchical, high-resolution shoreline database, Journal of Geophysical Research: Solid Earth, 101(B4): 8741-8743. https://doi.org/10.1029/96JB00104   DOI
3 Park, J.S., D.K. Kang, H.S. Kim, M.R. Kim, and S.H. Cho, 2018. A study on the estimation of underwater shipping noise using automatic identification system data, The Journal of the Acoustical Society of Korea, 37(3): 129-138 (in Korean with English abstract). https://doi.org/10.7776/ASK.2018.37.3.129   DOI
4 Deng, C., Z. Cao, Z. Fang, and Z. Yu, 2013. Ship detection from optical satellite image using optical flow and saliency, Proc. of MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, vol. 8921, pp. 100-106. https://doi.org/10.1117/12.2031115   DOI
5 Greidanus, H., M. Alvarez, T. Eriksen, and V. Gammieri, 2015. Completeness and accuracy of a wide-area maritime situational picture based on automatic ship reporting systems, The Journal of Navigation, 69(1): 156-168. https://doi.org/10.1017/S0373463315000582   DOI
6 Han, J.R., 2021. A Spatio-Temporal Variation Pattern Analysis of Fishing Activity in the Jeju Sea of Korea Using V-Pass data, Dissertation of Master Degree, Pukyong National University, Busan, Korea.
7 Heiselberg, H., 2016. A direct and fast methodology for ship recognition in Sentinel-2 multispectral imagery, Remote Sensing, 8(12): 1033. https://doi.org/10.3390/rs8121033   DOI
8 Hijmans, R.J. and J. Etten, 2012. raster: Geographic analysis and modeling with raster data, R package version 2.0-12. https://rspatial.org/raster
9 Ho, T.K., 1995. Random decision forests, Proc. of 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Aug. 14-16, vol. 1, pp. 278-282. https://doi.org/10.1109/ICDAR.1995.598994   DOI
10 Hong, D.B. and C.S. Yang, 2014. Classification of Passing Vessels Around the Ieodo Ocean Research Station Using Automatic Identification System (AIS): November 21-30, 2013, Journal of the Korean Society for Marine Environment and Energy, 17(4): 297-305 (in Korean with English abstract). https://doi.org/10.7846/JKOSMEE.2014.17.4.297   DOI
11 Inoue, K. and K. Hara, 1973. Detection Days and Level of Marine Traffic Volume, Japan Institute Navigation, 50: 1-8.   DOI
12 Lee, J.S. and J.W. Song, 2017. A Study on the Degree of Collision Risk through Analysing the Risk Attitude of Vessel Traffic Service Operator, Journal of Navigation and Port Research, 41(3): 93-102 (in Korean with English abstract). https://doi.org/10.5394/KINPR.2017.41.3.93   DOI
13 Jeon, H.K. and Y.C. Jung, 2018. Development of a Collision Risk Assessment System for Optimum Safe Route, Journal of the Korean Society of Marine Environment & Safety, 24(6): 670-678 (in Korean with English abstract). https://doi.org/10.7837/kosomes.2018.24.6.670   DOI
14 Johansson, L., J.P. Jalkanen, J. Kalli, and J. Kukkonen, 2013. The evolution of shipping emissions and the costs of regulation changes in the northern EU area, Atmospheric Chemistry and Physics, 13(22): 11375-11389. https://doi.org/10.5194/acp-13-11375-2013   DOI
15 Kazimierski, W. and A. Stateczny, 2015. Radar and automatic identification system track fusion in an electronic chart display and information system, Journal of Navigation, 68(6): 1141-1154. https://doi.org/10.1017/S0373463315000405   DOI
16 QGIS Development Team, 2019. QGIS Geographic Information System, Open Source Geospatial Foundation Project, http://qgis.osgeo.org, Accessed on Aug. 23, 2022.
17 Liu, Z., L. Yuan, L. Weng, and Y. Yang, 2017. A high resolution optical satellite image dataset for ship recognition and some new baselines, Proc. of 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM), Porto, Portugal, Feb. 24-26, vol. 1, pp. 324-331. https://doi.org/10.5220/0006120603240331   DOI
18 MOF (Ministry of Oceans and Fisheries), 2022. 3rd Enforcement Decree of The Maritime Safety Act, https://www.mof.go.kr/article/view.do?articleKey=44623&searchCategory=%ED%95%B4%EC%82%AC%EC%95%88%EC%A0%84%EA%B5%AD&boardKey=81&menuKey=1042¤tPageNo=1, Accessed on Aug. 23, 2022.
19 Park, S.Y., E.K. Seo, D.H. Kang, J.H. Im, and M.I. Lee, 2018. Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia, Remote Sensing, 10(11): 1811. https://doi.org/10.3390/rs10111811   DOI
20 Pedregosa. F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, 2011. Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12: 2825-2830.
21 Xiaoyang, X., Q. Xu, and H. Lei, 2016. Fast ship detection from optical satellite images based on ship distribution probability analysis, Proc. of 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Guangzhou, China, Jul. 4-6, pp. 97-101. https://doi.org/10.1109/EORSA.2016.7552774   DOI
22 Yoo, C.H., D.H. Han, J.H. Im, and B. Bechtel, 2019. Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images, ISPRS Journal of Photogrammetry and Remote Sensing, 157: 155-170. https://doi.org/10.1016/j.isprsjprs.2019.09.009   DOI
23 Park, J.W., J.S. Jeong, and Y.S. Park, 2021a. Ship Trajectory Prediction Based on Bi-LSTM Using Spectral-Clustered AIS Data, Journal of Marine Science and Engineering, 9(9): 1037. https://doi.org/10.3390/jmse9091037   DOI
24 Lee, S.J. and K.J. Lee, 2021. Efficient Generation of Artificial Training DB for Ship Detection Using Satellite SAR Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 11764-11774. https://doi.org/10.1109/JSTARS.2021.3128184   DOI
25 Emmans, T., C. Amrit, A. Abdi, and M. Ghosh, 2021. The promises and perils of Automatic Identification System data, Expert Systems with Applications, 178: 114975. https://doi.org/10.1016/j.eswa.2021.114975   DOI
26 Harati-Mokhtari, A., A. Wall, P. Brooks, and J. Wang, 2007. Automatic Identification System (AIS): data reliability and human error implications, The Journal of Navigation, 60(3): 373-389. https://doi.org/10.1017/S0373463307004298   DOI
27 Jeon, H.K. and C.S. Yang, 2021, Enhancement of Ship Type Classification from a Combination of CNN and KNN, Electronics, 10(10): 1169. https://doi.org/10.3390/electronics10101169   DOI
28 Jubelin, G. and A. Khenchaf, 2014. A unified algorithm for ship detection on optical and SAR spaceborne images, Proc. of Image and Signal Processing for Remote Sensing XX, Amsterdam, Netherlands, Sep. 22-25, vol. 9244, pp. 318-326. https://doi.org/10.1117/12.2067154   DOI
29 Kim, D.W., J.S. Park, and Y.S. Park, 2011. Comparison Analysis between the IWRAP and the ES Model in Ulsan Waterway, Journal of Navigation and Port Research, 35(4): 281-287 (in Korean with English abstract). https://doi.org/10.5394/KINPR.2011.35.4.281   DOI
30 Lee, J.S. and I.S. Cho, 2022. Extracting the Maritime Traffic Route in Korea Based on Probabilistic Approach Using Automatic Identification System Big Data, Applied Sciences, 12(2): 635 (in Korean with English abstract). https://doi.org/10.3390/app12020635   DOI
31 Lee, Y.J., D.H. Han, M.H. Ahn, J.H. Im, and S.J. Lee, 2019. Retrieval of total precipitable water from Himawari-8 AHI data: A comparison of random forest, extreme gradient boosting, and deep neural network, Remote Sensing, 11(15):1741. https://doi.org/10.3390/rs11151741   DOI
32 MOF (Ministry of Oceans and Fisheries), 2014. https://www.mof.go.kr/article/view.do?articleKey=4985&boardKey=27&menuKey=322¤tPagNo=1, Accessed on Aug. 23, 2022.
33 MOF (Ministry of Oceans and Fisheries), 2019. Statistic System of Ministry of Oceans and Fisheries in Korea, https://www.mof.go.kr/statPortal/main/portalMain.do, Accessed on Aug. 23, 2022.
34 Park, J.H., 2015. Analysis method for maritime accident using automatic identification system, Master's thesis, Korea University, Seoul, Republic of Korea.
35 Park, J.H., H.K. Jeon, and C.S. Yang, 2021b. Hidden Markov Model (HMM)-Based Fishing Activity Prediction Using V-Pass Data, Journal of Coastal Disaster Prevention, 8(4): 221-227. http://doi.org/10.20481/kscdp.2021.8.4.221   DOI