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- Title
A Fast Internal Wave Detection Method Based on PCANet for Ocean Monitoring.
- Authors
Wang, Shengke; Dong, Qinghong; Duan, Lianghua; Sun, Yujuan; Jian, Muwei; Li, Jianzhong; Dong, Junyu
- Abstract
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.
- Subjects
INTERNAL waves; OCEAN; DEEP learning; MULTIPLE correspondence analysis (Statistics); FINITE element method
- Publication
Journal of Intelligent Systems, 2019, Vol 28, Issue 1, p103
- ISSN
0334-1860
- Publication type
Article
- DOI
10.1515/jisys-2017-0033