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Cited by 13Ocean Waves and Remote SensingAdvanced Neural Network ApplicationsCoastal and Marine Dynamics

A Novel Deep Learning Model for Sea State Classification Using Visual-Range Sea Images

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Umair, Muhammad, Hashmani, Manzoor Ahmed, Hussain Rizvi, Syed Sajjad, Taib, Hasmi, Abdullah, Mohd Nasir, Memon, Mehak Maqbool·2022·Symmetry
DOI 10.3390/sym14071487
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Abstract

Wind-waves exhibit variations both in shape and steepness, and their asymmetrical nature is a well-known feature. One of the important characteristics of the sea surface is the front-back asymmetry of wind-wave crests. The wind-wave conditions on the surface of the sea constitute a sea state, which is listed as an essential climate variable by the Global Climate Observing System and is considered a critical factor for structural safety and optimal operations of offshore oil and gas platforms. Methods such as statistical representations of sensor-based wave parameters observations and numerical modeling are used to classify sea states. However, for offshore structures such as oil and gas platforms, these methods induce high capital expenditures (CAPEX) and operating expenses (OPEX), along with extensive computational power and time requirements. To address this issue, in this paper, we propose a novel, low-cost deep learning-based sea state classification model using visual-range sea images. Firstly, a novel visual-range sea state image dataset was designed and developed for this purpose. The dataset consists of 100,800 images covering four sea states. The dataset was then benchmarked on state-of-the-art deep learning image classification models. The highest classification accuracy of 81.8% was yielded by NASNet-Mobile. Secondly, a novel sea state classification model was proposed. The model took design inspiration from GoogLeNet, which was identified as the optimal reference model for sea state classification. Systematic changes in GoogLeNet’s inception block were proposed, which resulted in an 8.5% overall classification accuracy improvement in comparison with NASNet-Mobile and a 7% improvement from the reference model (i.e., GoogLeNet). Additionally, the proposed model took 26% less training time, and its per-image classification time remains competitive.

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  1. Asariotis, R.; Assaf, M.; Benamara, H.; Hoffmann, J.; Premti, A.; Rodríguez, L.; Weller, M.; Youssef, F. Review of Maritime Transport 2018; United Nations Publications: New York, NY, USA, 2018.
  2. Pradeepkiran, J.A. Aquaculture Role in Global Food Security with Nutritional Value: A Review. Transl. Anim. Sci. 2019, 3, 903-910. [CrossRef] [PubMed] · DOI
  3. Leposa, N. Problematic Blue Growth: A Thematic Synthesis of Social Sustainability Problems Related to Growth in the Marine and Coastal Tourism. Sustain. Sci. 2020, 15, 1233-1244. [CrossRef] · DOI
  4. Leykin, I.A.; Donelan, M.A.; Mellen, R.H.; McLaughlin, D.J. Asymmetry of Wind Waves Studied in a Laboratory Tank. Nonlinear Process. Geophys. 1995, 2, 280-289. [CrossRef] · DOI
  5. Dosaev, A.; Troitskaya, Y.I.; Shrira, V.I. On the Physical Mechanism of Front-Back Asymmetry of Non-Breaking Gravity-Capillary Waves. J. Fluid Mech. 2021, 906. [CrossRef] · DOI
  6. White, L.H.; Hanson, J.L. Automatic Sea State Calculator. Ocean. Conf. Rec. 2000, 3, 1727-1734.
  7. Dodet, G.; Piolle, J.F.; Quilfen, Y.; Abdalla, S.; Accensi, M.; Ardhuin, F.; Ash, E.; Bidlot, J.R.; Gommenginger, C.; Marechal, G.; et al. The Sea State CCI Dataset v1: Towards a Sea State Climate Data Record Based on Satellite Observations. Earth Syst. Sci. Data 2020, 12, 1929-1951. [CrossRef] · DOI
  8. Vannak, D.; Liew, M.S.; Yew, G.Z. Time Domain and Frequency Domain Analyses of Measured Metocean Data for Malaysian Waters. Int. J. Geol. Environ. Eng. 2013, 7, 549-554.
  9. Umair, M.; Hashmani, M.A.; Hasan, M.H.B. Survey of Sea Wave Parameters Classification and Prediction Using Machine Learning Models. In Proceedings of the 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS), Ipoh, Malaysia, 19 September 2019; pp. 1-6. [CrossRef] · DOI
  10. Thomas, T.J.; Dwarakish, G.S. Numerical Wave Modelling-A Review. Aquat. Procedia 2015, 4, 443-448. [CrossRef] · DOI
  11. Zhang, Y.; Tian, Z.; Lei, Y.; Wang, T.; Patel, P.; Jani, A.B.; Curran, W.J.; Liu, T.; Yang, X. Automatic Multi-Needle Localization in Ultrasound Images Using Large Margin Mask RCNN for Ultrasound-Guided Prostate Brachytherapy. Phys. Med. Biol. 2020, 65, 205003. [CrossRef] · DOI
  12. González, R.E.; Muñoz, R.P.; Hernández, C.A. Galaxy Detection and Identification Using Deep Learning and Data Augmentation. Astron. Comput. 2018, 25, 103-109. [CrossRef] · DOI
  13. Zhang, Y.; An, R.; Liu, S.; Cui, J.; Shang, X. Predicting and Understanding Student Learning Performance Using Multi-Source Sparse Attention Convolutional Neural Networks. IEEE Trans. Big Data 2021, 1-14. [CrossRef] · DOI
  14. Pan, M.; Liu, Y.; Cao, J.; Li, Y.; Li, C.; Chen, C.H. Visual Recognition Based on Deep Learning for Navigation Mark Classification. IEEE Access 2020, 8, 32767-32775. [CrossRef] · DOI
  15. Escorcia-Gutierrez, J.; Gamarra, M.; Beleño, K.; Soto, C.; Mansour, R.F. Intelligent Deep Learning-Enabled Autonomous Small Ship Detection and Classification Model. Comput. Electr. Eng. 2022, 100, 107871. [CrossRef] · DOI
  16. Singleton, F. The Beaufort Scale of Winds -Its Relevance, and Its Use by Sailors. Weather 2008, 63, 37-41. [CrossRef] · DOI
  17. Li, X.M.; Huang, B.Q. A Global Sea State Dataset from Spaceborne Synthetic Aperture Radar Wave Mode Data. Sci. Data 2020, 7, 261. [CrossRef] · DOI
  18. Loizou, A.; Christmas, J. Estimating Pixel to Metre Scale and Sea State from Remote Observations of the Ocean Surface. Int. Geosci. Remote Sens. Symp. 2018, 2018, 3513-3516. [CrossRef] · DOI
  19. Cheng, X.; Li, G.; Skulstad, R.; Chen, S.; Hildre, H.P.; Zhang, H. Modeling and Analysis of Motion Data from Dynamically Positioned Vessels for Sea State Estimation. In Proceedings of the IEEE International Conference on Robotics and Automation, Montreal, QC, Canada, 20-24 May 2019; Volume 2019, pp. 6644-6650.
  20. Wang, L.; Hong, L. Ocean Wave Height Inversion under Low Sea State from Horizontal Polarized X-Band Nautical Radar Images. J. Spat. Sci. 2021, 66, 75-87. [CrossRef] · DOI
  21. Ampilova, N.; Soloviev, I.; Kotopoulis, A.; Pouraimis, G.; Frangos, P. On the Application of Multifractal Methods for the Analysis of Sea Surface Images Related to Sea State Determination. In Proceedings of the 14th International Conference onCommunications, Electromagnetics and Medical Applications, Florence, Italy, 2-3 October 2019; pp. 32-35.
  22. Almar, R.; Bergsma, E.W.J.; Catalan, P.A.; Cienfuegos, R.; Suarez, L.; Lucero, F.; Lerma, A.N.; Desmazes, F.; Perugini, E.; Palmsten, M.L.; et al. Sea State from Single Optical Images: A Methodology to Derive Wind-Generated Ocean Waves from Cameras, Drones and Satellites. Remote Sens. 2021, 13, 679. [CrossRef] · DOI
  23. Rikka, S.; Pleskachevsky, A.; Uiboupin, R.; Jacobsen, S. Sea State Parameters in Highly Variable Environment of Baltic Sea from Satellite Radar Images. In Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, 23-28 July 2017; pp. 2965-2968.
  24. Umair, M.; Hashmani, M.A. Towards Development of Visual-Range Sea State Image Dataset for Deep Learning Models. In Proceedings of the 2021 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), Bandung, Indonesia, 1-2 December 2021; pp. 23-27.
  25. Tu, F.; Ge, S.S.; Choo, Y.S.; Hang, C.C. Sea State Identification Based on Vessel Motion Response Learning via Multi-Layer Classifiers. Ocean Eng. 2018, 147, 318-332. [CrossRef] · DOI
  26. Cheng, X.; Li, G.; Skulstad, R.; Zhang, H.; Chen, S. SpectralSeaNet: Spectrogram and Convolutional Network-Based Sea State Estimation. In Proceedings of the IECON 2020 the 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 18-21 October 2020; pp. 5069-5074.
  27. Liu, N.; Jiang, X.; Ding, H.; Xu, Y.; Guan, J. Wave Height Inversion and Sea State Classification Based on Deep Learning of Radar Sea Clutter Data. In Proceedings of the 10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021, Xi'an, China, 14-17 October 2021; pp. 34-39.
  28. Johnson, J.M.; Khoshgoftaar, T.M. Survey on Deep Learning with Class Imbalance. J. Big Data 2019, 6. [CrossRef] · DOI · Paper page
  29. Zhang, K.; Yu, Z.; Qu, L. Application of Deep Learning in Sea States Images Classification. In Proceedings of the 2021 7th Annual International Conference on Network and Information Systems for Computers, Guiyang, China, 23-25 July 2021; pp. 976-979. [CrossRef] · DOI
  30. Patino, L.; Cane, T.; Vallee, A.; Ferryman, J. PETS 2016: Dataset and Challenge. In Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Las Vegas, NV, USA, 26-1 July 2016; pp. 1240-1247. [CrossRef] · DOI
  31. Hashmani, M.A.; Umair, M. A Novel Visual-Range Sea Image Dataset for Sea Horizon Line Detection in Changing Maritime Scenes. J. Mar. Sci. Eng. 2022, 10, 193. [CrossRef] · DOI
  32. Suresh, R.R.V.; Annapurnaiah, K.; Reddy, K.G.; Lakshmi, T.N.; Balakrishnan Nair, T.M. Wind Sea and Swell Characteristics off East Coast of India during Southwest Monsoon. Int. J. Ocean. Oceanogr. 2010, 4, 35-44.
  33. Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; Li, F.-F. Imagenet: A Large-Scale Hierarchical Image Database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20-25 June 2009; pp. 248-255.
  34. Olsen, A.; Konovalov, D.A.; Philippa, B.; Ridd, P.; Wood, J.C.; Johns, J.; Banks, W.; Girgenti, B.; Kenny, O.; Whinney, J.; et al. DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning. Sci. Rep. 2019, 9, 2058. [CrossRef] · DOI
  35. Shorten, C.; Khoshgoftaar, T.M. A Survey on Image Data Augmentation for Deep Learning. J. Big Data 2019, 6, 60. [CrossRef] · DOI · Paper page
  36. Buslaev, A.; Iglovikov, V.I.; Khvedchenya, E.; Parinov, A.; Druzhinin, M.; Kalinin, A.A. Albumentations: Fast and Flexible Image Augmentations. Information 2020, 11, 125. [CrossRef] · DOI · Paper page
  37. Simonyan, K.; Zisserman, A. Very Deep Convolutional Networks for Large-Scale Image Recognition. In Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7-9 May 2015; pp. 1-14.
  38. Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going Deeper with Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015; pp. 1-9.
  39. Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv 2017, arXiv:1704.04861. · arXiv
  40. Chollet, F. Xception: Deep Learning with Depthwise Separable Convolutions. In Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21-26 July 2017; pp. 1251-1258.
  41. Redmon, J.; Farhadi, A. YOLO9000: Better, Faster, Stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21-26 July 2017; pp. 7263-7271.
  42. Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. Densely Connected Convolutional Networks. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21-26 July 2017; pp. 2261-2269. [CrossRef] · DOI · Paper page
  43. Zoph, B.; Vasudevan, V.; Shlens, J.; Le, Q.V. Learning Transferable Architectures for Scalable Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-23 June 2018; pp. 8697-8710.
  44. Zhang, X.; Zhou, X.; Lin, M.; Sun, J. Shufflenet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18-23 June 2018; pp. 6848-6856.
  45. Tan, M.; Le, Q.V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 9-15 June 2019; pp. 10691-10700.
  46. Park, S.; Kwak, N. Analysis on the Dropout Effect in Convolutional Neural Networks. In Lecture Notes in Computer Science (LNCS); Springer: Cham, Switzerland, 2017; Volume 10112, pp. 189-204. [CrossRef] · DOI

Cited by

Sorted by citations

Authors
Umair, M., Hashmani, M. A., Hussain Rizvi, S. S., Taib, H., Abdullah, M. N., & Memon, M. M.
Affiliations
  • Universiti Teknologi Petronas (MY)
  • Department of Computer Science, Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology, Karachi 75600, Pakistan
  • Petronas (Malaysia) (MY)
Corresponding author
Umair, Muhammad
ORCID
Venue
Symmetry
Publisher
MDPI AG
Published
2022-07-20
Volume
14
Issue
7
Pages
1487
ISSN
2073-8994 (ISSN-L)
DOI
10.3390/sym14071487
OpenAlex
W4286502880
Cited by
13
References
2
Citations by year
2026: 5 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 1
FWCI
0.99
Type
journal article
Language
en
Open access
Licence
Locations
  • Symmetry (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Yayasan Universiti Teknologi PETRONAS (YUTP-FRG-2019, 015LC0-158)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Ocean Waves and Remote Sensing
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Earth and Planetary Sciences › Earth-Surface Processes › Coastal and Marine Dynamics
Keywords
wind waves, GoogLeNet, NASNetMobile, image classification, offshore oil and gas platforms

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