Residual network-based ocean wave modelling from satellite images using ensemble Kalman filter

Nonlinear ocean waves have a significant impact on the functioning of several offshore activities. Predicting the internal ocean waves plays a crucial role on submarine and ship operations. Data assimilation is a mechanism in which data observed is interpreted, processed and adapted. The existing works for estimating the future atmospheric condition are highly dependent on the exact initial state, which mostly differ from the observation. This paper proposes modelling of internal ocean waves using automatic internal wave detection and data assimilation. Ensemble Kalman filtering method is used to model ocean waves. The proposed system is focused on satellite images. The images are pre-processed for speckle noise using adaptive filters. Enhanced residual network is used for edge detection. Unlike the existing edge detection methods that have high complexity, this enhanced residual network works with low complexity and makes a direct mapping between the input wave image and wave edge. Finally, the potential edges of the internal wave are detected and adapted using ensemble Kalman filter. Adaptive thresholding technique is used to determine the appropriate threshold to segregate objects from background. The proposed enhanced edge detection model is compared w.r.t to the parameters weighted cross-entropy loss function, accuracy and root mean squared error with canny edge detection and proved to be better. The detection of internal wave is demonstrated, and the accuracy of the approach is 91% with low RMSE when compared to existing works.