Wave height classification via deep learning using monoscopic ocean videos

Abstract

The ocean environment has a significant influence on aquaculture, marine transportation, and the construction of coastal and offshore structures. In this regard, we describe a deep-learning based wave height classification method using monoscopic ocean videos. Images and videos as input for learning were obtained using a monoscopic camera, and the wave height was measured using an acoustic Doppler current profiler installed in the southwestern area of Korea. Initially, the sea states and average wave height were classified from single snapshots using only a convolutional neural network (CNN). Subsequently, the average wave height was classified from sequential snapshots using a combined deep learning algorithm with long short-term memory (LSTM) and CNN. The combined network with an appropriate data augmentation was found to be effective and showed good performance. The proposed method can be applied in future studies to identify a wider range of wave heights and wave breaking phenomena.