Sea state from ocean video with singular spectrum analysis and extended Kalman filter

Abstract A method for estimating key parameters of ocean waves (the dominant frequency and the significant wave height) from uncalibrated monoscopic video is proposed, based on temporal variation field, specifically time series pixel intensities. The methodology tracks principal component movement water in video, which we propose associated with ocean. To accomplish this, singular spectrum analysis algorithm extended Kalman filter are used. Then, shape an empirical used order to translate output into a height estimation.