Abstract
In this paper, we design a novel underwater hyperspectral imaging technique for deep-sea mining detection. The spectral sensitivity peaks are in the region of the visible spectrum, 580, 650, 720, 800 nm. In addition, to the underwater objects recognition, because of the physical properties of the medium, the captured images are distorted seriously by scattering, absorption and noise effect. Scattering is caused by large suspended particles, such as in turbid water, which contains abundant particles, algae, and dissolved organic compounds. In order to resolve these problems of recognizing mineral accurately, fast and effectively, an identifying and classifying algorithm is proposed in this paper. We take the following steps: firstly, through image preprocessing, hyperspectral images are gained by using denoising, smoothness, image erosion. After that, we segment the cells by the method of the modified active contour method. These methods are designed for real-time execution on limited-memory platforms, and are suitable for detecting underwater objects in practice. The Initial results are presented and experiments demonstrate the effectiveness of the proposed imaging system.
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Acknowledgements
This work was supported by Leading Initiative for Excellent Young Researcher (LEADER) of MEXT-Japan (16809746), Grants-in-Aid for Scientific Research of JSPS (17K14694), Research Fund of SKL of Ocean Engineering in Shanghai Jiaotong University (1315; 1510), Research Fund of SKL of Marine Geology in Tongji University (MGK1608), Research Fund of The Telecommunications Advancement Foundation, Open Collaborative Research Program at National Institute of Informatics Japan (NII), Japan-China Scientific Cooperation Program (6171101454), and International Exchange Program of National Institute of Information and Communications (NICT), and Fundamental Research Developing Association for Shipbuilding and Offshore.
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Lu, H., Zheng, Y., Hatano, K., Li, Y., Nakashima, S., Kim, H. (2020). Hyperspectral Images Segmentation Using Active Contour Model for Underwater Mineral Detection. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_50
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DOI: https://doi.org/10.1007/978-3-030-04946-1_50
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