Abstract
As internal wave is a universal geophysical phenomenon in stratified fluids, study of internal wave features in the coastal ocean is one of the most important tasks in physical oceanography. Traditionally, various internal wave detection methods, such as acoustic, optical, electrical based techniques and SAR based technique have been proposed. However, those methods need expensive measuring devices and often face the difficulties of the installation when deployed in the ocean. With the development of machine learning recently, internal wave detection based on computer vision and machine learning becomes a hot topic. In this paper, a framework for internal waves detection based on PCANet which is a feature learning deep network is proposed. First, we collect simulated internal wave images and non-internal wave images, then we give a label to each image to indicate whether it includes internal waves or not. Finally, we train a discrimination model with PCANet and predict new images at the test stage. Experiment results demonstrated the feasibility of the technique for internal wave detection.
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Acknowledgments
This work is supported by the Natural Science Foundation of China (NSFC) Grants 61301241, 61602229, 61403353, 61501417 and 61271405; Natural Science Foundation of Shandong (ZR2015FQ011; ZR2014FQ023); China Postdoctoral Science Foundation funded project (2016M590659); Qingdao Postdoctoral Science Foundation funded project(861605040008); The Fundamental Research Funds for the Central Universities (201511008, 30020084851);
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Dong, Q., Wang, S., Jian, M., Sun, Y., Dong, J. (2018). An Internal Waves Detection Method Based on PCANet for Images Captured from UAV. In: Wan, J., et al. Cloud Computing, Security, Privacy in New Computing Environments. CloudComp SPNCE 2016 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 197. Springer, Cham. https://doi.org/10.1007/978-3-319-69605-8_22
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DOI: https://doi.org/10.1007/978-3-319-69605-8_22
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