Journal of Intelligent Systems (Jan 2019)

A Fast Internal Wave Detection Method Based on PCANet for Ocean Monitoring

  • Wang Shengke,
  • Dong Qinghong,
  • Duan Lianghua,
  • Sun Yujuan,
  • Jian Muwei,
  • Li Jianzhong,
  • Dong Junyu

DOI
https://doi.org/10.1515/jisys-2017-0033
Journal volume & issue
Vol. 28, no. 1
pp. 103 – 113

Abstract

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Research on internal waves in the coastal ocean is one of the most important tasks both in physical oceanography and ocean monitoring network. Currently, how to quickly and accurately detect the ocean internal waves from the huge ocean surface is still a challenging issue. In this paper, we model the ocean internal wave detection as a task of region classification for texture images and then propose a rapid internal waves detection method based on a deep learning framework (PCANet). In the proposed method, two models have been trained: one is the deep feature representation model, which combines principal component analysis (PCA), binary hashing, and block-wise histograms and can extract more distinguishing features than handcraft feature. Moreover, because the filter learning in PCANet does not require regularized parameters and numerical optimization solver, the training process of the representation model is very fast. The other one is a classification model based on a linear support vector machine. The object proposal method has been applied to get the possible candidates when analyzing a captured image, which dramatically decreases the searching time. Experiment results on the data set captured by unmanned aerial vehicles verify the speed ability and effectiveness of the proposed method.

Keywords