Search

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]

As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method. [ABSTRACT FROM AUTHOR]

Determining the altitude of side-scan sonar (SSS) above the seabed is critical to correct the geometric distortions in the sonar images. Usually, a technology named bottom tracking is applied to estimate the distance between the sonar and the seafloor. However, the traditional methods for bottom tracking often require pre-defined thresholds and complex optimization processes, which make it difficult to achieve ideal results in complex underwater environments without manual intervention. In this paper, a universal automatic bottom tracking method is proposed based on semantic segmentation. First, the waterfall images generated from SSS backscatter sequences are labeled as water column (WC) and seabed parts, then split into specific patches to build the training dataset. Second, a symmetrical information synthesis module (SISM) is designed and added to DeepLabv3+, which not only weakens the strong echoes in the WC area, but also gives the network the capability of considering the symmetry characteristic of bottom lines, and most importantly, the independent module can be easily combined with any other neural networks. Then, the integrated network is trained with the established dataset. Third, a coarse-to-fine segmentation strategy with the well-trained model is proposed to segment the SSS waterfall images quickly and accurately. Besides, a fast bottom line search algorithm is proposed to further reduce the time consumption of bottom tracking. Finally, the proposed method is validated by the data measured with several commonly used SSSs in various underwater environments. The results show that the proposed method can achieve the bottom tracking accuracy of 1.1 pixels of mean error and 1.26 pixels of standard deviation at the speed of 2128 ping/s, and is robust to interference factors. [ABSTRACT FROM AUTHOR]