Research Article
An Internal Waves Detection Method Based on PCANet for Images Captured from UAV
@INPROCEEDINGS{10.1007/978-3-319-69605-8_22, author={Qinghong Dong and Shengke Wang and Muwei Jian and Yujuan Sun and Junyu Dong}, title={An Internal Waves Detection Method Based on PCANet for Images Captured from UAV}, proceedings={Cloud Computing, Security, Privacy in New Computing Environments. 7th International Conference, CloudComp 2016, and First International Conference, SPNCE 2016, Guangzhou, China, November 25--26, and December 15--16, 2016, Proceedings}, proceedings_a={CLOUDCOMP}, year={2017}, month={11}, keywords={}, doi={10.1007/978-3-319-69605-8_22} }- Qinghong Dong
Shengke Wang
Muwei Jian
Yujuan Sun
Junyu Dong
Year: 2017
An Internal Waves Detection Method Based on PCANet for Images Captured from UAV
CLOUDCOMP
Springer
DOI: 10.1007/978-3-319-69605-8_22
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.
