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Fixed video camera systems are consistently prone to importune motions over time due to either thermal effects or mechanical factors. Even subtle displacements are mostly overlooked or ignored, although they can lead to large geo-rectification errors. This paper describes a simple and efficient method to stabilize an either continuous or sub-sampled image sequence based on feature matching and sub-pixel cross-correlation techniques. The method requires the presence and identification of different land-sub-image regions containing static recognizable features, such as corners or salient points, referred to as keypoints. A Canny edge detector ( C E D ) is used to locate and extract the boundaries of the features. Keypoints are matched against themselves after computing their two-dimensional displacement with respect to a reference frame. Pairs of keypoints are subsequently used as control points to fit a geometric transformation in order to align the whole frame with the reference image. The stabilization method is applied to five years of daily images collected from a three-camera permanent video system located at Anglet Beach in southwestern France. Azimuth, tilt, and roll deviations are computed for each camera. The three cameras showed motions on a wide range of time scales, with a prominent annual signal in azimuth and tilt deviation. Camera movement amplitude reached up to 10 pixels in azimuth, 30 pixels in tilt, and 0.4° in roll, together with a quasi-steady counter-clockwise trend over the five-year time series. Moreover, camera viewing angle deviations were found to induce large rectification errors of up to 400 m at a distance of 2.5 km from the camera. The mean shoreline apparent position was also affected by an approximately 10–20 m bias during the 2013/2014 outstanding winter period. The stabilization semi-automatic method successfully corrects camera geometry for fixed video monitoring systems and is able to process at least 90% of the frames without user assistance. The use of the C E D greatly improves the performance of the cross-correlation algorithm by making it more robust against contrast and brightness variations between frames. The method appears as a promising tool for other coastal imaging applications such as removal of undesired high-frequency movements of cameras equipped in unmanned aerial vehicles (UAVs).

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In this study, three months of records (January–March 2010) that were acquired by a geodetic Global Navigation Satellite Systems (GNSS) station from the permanent network of RGP (Réseau GNSS Permanent), which was deployed by the French Geographic Institute (IGNF), located in Socoa, in the south of the Bay of Biscay, were used to determine the tide components and identify the signature of storms on the signal to noise ratio (SNR) during winter 2010. The Xynthia storm hit the French Atlantic coast on the 28th of February 2010, causing large floods and damages from the Gironde to the Loire estuaries. Blind separation of the tide components and of the storm signature was achieved while using both a singular spectrum analysis (SSA) and a continuous wavelet transform (CWT). A correlation of 0.98/0.97 and root mean square error (RMSE) of 0.21/0.28 m between the tide gauge records of Socoa and our estimates of the sea surface height (SSH) using the SSA and the CWT, respectively, were found. Correlations of 0.76 and 0.7 were also obtained between one of the modes from the SSA and atmospheric pressure from a meteorological station and a mode of the SSA. Particularly, a correlation reaches to 0.76 when using both the tide residual that is associated to surges and atmospheric pressure variation.