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To obtain the high-resolution seabed topography and overcome the limitations of existing topography reconstruction methods in requiring external bathymetric data and ignoring the effects of sediment variations and Side-Scan Sonar (SSS) image quality, this study proposes a method of reconstructing seabed topography from SSS images with a self-constraint condition. A reconstruction model is deduced by Lambert's law and the seabed scattering model. A bottom tracking method is put forward to get the along-track SSS towfish heights and the initial seabed topography in the SSS measuring area is established by combining the along-track towfish heights, towfish depths and tidal levels obtained from Global Navigation Satellite System (GNSS). The complete process of reconstructing seabed topography is given by taking the initial topography as self-constraint and the high-resolution seabed topography is finally obtained. Experiments verified the proposed method by the data measured in Zhujiang River, China. The standard deviation of less than 15 cm is achieved and the resolution of the reconstructed topography is about 60 times higher than that of the Digital Elevation Model (DEM) established by bathymetric data. The effects of noise, suspended bodies, refraction of wave in water column, sediment variation, the determination of iteration termination condition as well as the performance of the proposed method under these effects are discussed. Finally, the conclusions are drawn out according to the experiments and discussions. The proposed method provides a simple and efficient way to obtain high-resolution seabed topography from SSS images and is a supplement but not substitution for the existing bathymetric methods. [ABSTRACT FROM AUTHOR]

The angular response (AR) effect which reduces multibeam backscatter image quality significantly cannot be removed by the traditional AR correction methods effectively. Therefore, the paper puts forward two self-adaptive AR correction methods, namely the AR modeling method and the AR clustering method. The former uses the AR parameters of the average AR curve for modeling and correction, while the latter uses the AR curve of each seabed sediment for AR correction. Both methods overcome the defects of the traditional methods, and improve the image quality obviously. The validities of the methods have been verified by the experiments. [ABSTRACT FROM AUTHOR]

Sailing‐circle GPS‐acoustic method, prevalently applied for measuring ocean floor crustal deformation, allows the precise positioning of an underwater transponder in a global frame. In the method, distance estimation requires prior information, such as transponder depth or incident angle. However, sometimes the prior information is inaccurate or absent. Under these circumstances, this study proposes two kinds of positioning methods that utilize an iteration composed of an inverse or direct ray tracing method and the least squares approach to constantly modify the initial data to obtain an accurate incident angle and coordinates of an underwater transponder. Even though an accurate incident angle can be got and there is no time delay or spatial‐temporal heterogeneities of SSP (sound speed profile), SSP density and geometric incident angle still influence ranging errors and PDOP (position dilution of precision) during distance intersection. In order to reduce the two kinds of impacts, this paper deduces a positioning error model based on the principle of sailing‐circle method. On the one hand, the proposed algorithms and model are verified by a simulated experiment for various SSP density, geometric incident angle, and transponder depth. On the other hand, in a practical experiment, average positioning accuracy of 0.025 m and vertical accuracy of −0.02 m are achieved, and the improved accuracy is about 85% higher than that of the ordinary sailing‐circle method. Plain Language Summary: GPS‐acoustic method is a kind of method that utilizes GPS, which is usually placed in our vehicle for navigation, and sonars, which can measure the distance between two points in water, for determining the position of a fixed point underwater. By continuously monitoring the location of this point, the position change is obtained and the change can help us predict earthquakes and study earthquakes. Among GPS‐acoustic methods, sailing‐circle method that the vessel equipped with GPS and sonars drives a circle centered on the underwater point can gain the best positioning accuracy. However, the method requires accurate prior information, such as transponder depth or incident angle. When the prior information is inaccurate or absent, accuracy will decrease and therefore, we propose two kinds of new sailing‐circle methods to deal with this situation. And it is noted that whether the prior information is accurate or not, there will be errors in the final positioning results caused by instruments, algorithms, and environments. Consequently, based on the principle of sailing‐circle method, we deduce the positioning error model to guide us design various parameters in a practical experiment, such as sailing radius, and improve the positioning accuracy. The ultimate goal of this paper is to improve the positioning accuracy of sailing‐circle method. To check the effectiveness of the two proposed methods and model, on the one hand, they are verified by a simulated experiment for various situations. On the other hand, in a practical experiment, positioning accuracy of the proposed method is about 85% higher than that of the ordinary method. Key Points: Two new sailing‐circle GPS‐acoustic methodsPositioning error model with respect to geometric incident angle (or sailing radius) and the density of sound speed profile is deducedGeometric incident angle should be controlled within 50° and the smaller the density of sound speed profile, the better [ABSTRACT FROM AUTHOR]

Side-scan sonar (SSS) is used for obtaining high-resolution seabed images, but with low position accuracy without using the Ultra Short Base Line (USBL) or Short Base Line (SBL). Multibeam echo sounder (MBES), which can simultaneously obtain high-accuracy seabed topography as well as seabed images with low resolution in deep water. Based on the complementarity of SSS and MBES data, this paper proposes a new method for acquiring high-resolution seabed topography and surface details that are difficult to obtain using MBES or SSS alone. Firstly, according to the common seabed features presented in both images, the Speeded-Up Robust Features (SURF) algorithm, with the constraint of image geographic coordinates, is adopted for initial image matching. Secondly, to further improve the matching performance, a template matching strategy using the dense local self-similarity (DLSS) descriptor is adopted according to the self-similarities within these two images. Next, the random sample consensus (RANSAC) algorithm is used for removing the mismatches and the SSS backscatter image geographic coordinates are rectified by the transformation model established based on the correct matched points. Finally, the superimposition of this rectified SSS backscatter image on MBES seabed topography is performed and the high-resolution and high-accuracy seabed topography and surface details can be obtained. [ABSTRACT FROM AUTHOR]