Tracking shoreline change using minimum convolution of Gaussian weight and squared differences

Abstract

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Detecting and responding appropriately to temporal changes in the shoreline is an important task for protecting coasts. Video monitoring has been utilized as a powerful tool for detecting shoreline changes. Existing shoreline-tracking methods include the threshold methods, colour intensity gradient methods, and neural networks, which involve ad-hoc assignment of the threshold values, drawing shore-normal transects, and heavy preliminary training for each coast with many data, respectively. The study applies a new boundary tracking method using Minimum Convolution of Gaussian Weight and Squared Differences (MCGWSD). The new method is fast and effective in a sense that it does not need ad-hoc threshold, drawing of transects, or pre-training. This method tracks boundary lines between two zones with no thickness by inversely tracking every pixel of the late image. The MCGWSD method is first examined for various image distortions, i.e. translation, linear deformation, angular deformation, and rotation of images. Images of a part of orange peel are chosen for the test, where a boundary line is artificially drawn, not necessarily following clear object boundary, but crosses over small patterns. The new method satisfactorily tracks the movement of boundary line at the tests. Then field video images of Jangsa Beach between 1 September 2020 and 15 September 2020, when typhoons Maysak and Haishen hit the coast, are examined to track the shoreline movement. Ground truth shoreline information at the coast during the time is not available, and results of existing colour intensity gradient method PIMACS are assumed true. According to PIMACS results on the beach width along two transects during the period, the shoreline underwent a movement up to 6 m. The new MCGWSD method tracks the shoreline position, and its results show good agreement with PIMACS results along two transects. The merits of the present method are that it produces shoreline change over the whole domain, and shore-normal transects are not needed. The present method effectively tracks the shoreline retreat or advance of as small as 1 pixel of image. The new method could be used for tracking shoreline change at arbitrary geometry even with sharp corners.

Keywords

• minimum convolution of Gaussian weight and squared differences (MCGWSD)
• shoreline detection method
• shoreline movement
• local dissimilarity index
• video monitoring