

FOLLOWUS
1. College of Automation and Electronic Engineering Qingdao University of Science & Technology
2. College of Electronic and Information Engineering Xi’an Jiaotong University
3. Shandong Entry-Exit Inspection and Quarantine Bureau
4. Department of Electronic Engineering Ocean University of China
Online First:28 May 2008,
Published:2008
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Zhao, W., Wei, H., Shi, C. et al. The recognition of ocean red tide with hyper-spectral-image based on EMD., Chin. J. Ocean. Limnol. 26, 137–141 (2008). https://doi.org/10.1007/s00343-008-0137-x
A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition (EMD)
from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general picture data
with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile
the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence
with which a red tide can be forecast.
Cui, T. W., J. Zhang, Y. Ma et al., 2002. Spectral characteristics analysis of red tide water in mesocosm experiment. SPIE Proceedings-Ocean Remote Sensing and Applications 4 892.
Fan, X. W., H. D. Zhang and X. W. Sun, 2003. The application of hyper-spectral data to the detection and identification of red tides. Remote Sensing for Land & Resources 15(1): 8–12.
Gordon, H. R. and A. Morel, 1983. Remote assessment of ocean color for interpretation of satellite visible imagery: a review. Lecture Notes on Costal and Estuarine Studies p.114
Ma, Y. 2003. The study on detecting technology of the red tide aerial remote sensing hyper-spectral. Doctoral thesis, the Graduate School of CAS, China.
Wu, Y. S., S. K. Zhang, X. Q. Zhang et al., 2002. Experimental study on the optical absorption property of yellow substance in seawater. Oceanologia et Limnologia Sinica 33(4): 402–406. (in Chinese)
Xie, X. R. and Y. H. Zhang, 2000. The technology and application of the spectral recognition and monitoring. Practical Measurement Technology 5: 5–8. (in Chinese)
Zhao, W. C., G. R. Ji, R. Nian et al., 2005. Blindly selecting method of training samples based hyper-spectral image’s intrinsic character for object recognition. Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology Suzhou, China, p. 113–116.
Zhao, W. C., X. B. Yu and F. X. Li, 2005. Microcalcification Patterns Recognition based Combination of Autoassociator and Classifier. Lecture Notes in Artificial Intelligence 3801: 1 045–1 050.
Zhao, W. C., G. R. Ji, L. J. Zhou et al., 2005. Study of recognition method of phytoplankton cell image. Computer Engineering 31(24): 143–145.
Zhao, W. C. and S. L. Sui. 2006. Multidimensional images clustering method based sensitive subspace. Dynamics of Continuous, Discrete and Impulsive Systems (Series B) 9: 524–529.
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