SpectralSeaNet: spectrogram and convolutional network-based sea state estimation

Abstract

Sea State is significant to the operations on the sea. The traditional model-based approaches need lots of knowledge of vessels, which limit the real-world use. This paper proposes a spectrogram-based deep learning model for sea state estimation (SpectralNet). In this model, the ship motion data is converted to spectrogram using short time Fourier transform (STFT). Unlike other methods, the spectrogram of each sensor will be combined to a new image. And then, a 2D convolutional neural network (CNN) is built as the classifier and the sea state can be identified. The experimental results show the proposed approach can achieve higher classification accuracy compared these methods applied directly in raw time series data. Through the comparison results of the proposed approach and the combination of spectrogram of different number of sensors, the proposed approach can achieve highest classification accuracy, and the classification accuracy is growing with the number of combined sensors. The sensitivity analysis finds the classification accuracy is easily influenced by the scale factor of images.