Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet

Jun Yan,Junxia Meng,Jianhu Zhao

doi:10.3390/rs13051024

Abstract

As widely applicated in many underwater research fields, conventional side-scan sonars require the sonar height to be at the seabed for geocoding seabed images. However, many interference factors, including compensation with unknown gains, suspended matters, etc., would bring difficulties in bottom detection. Existing methods need manual parameter setups or to use postprocessing methods, which limits automatic and real-time processing in complex situations. To solve this problem, a one-dimensional U-Net (1D-UNet) model for sea bottom detection of side-scan data and the bottom detection and tracking method based on 1D-UNet are proposed in this work. First, the basic theory of sonar bottom detection and the interference factors is introduced, which indicates that deep learning of the bottom is a feasible solution. Then, a 1D-UNet model for detecting the sea bottom position from the side-scan backscatter strength sequences is proposed, and the structure and implementation of this model are illustrated in detail. Finally, the bottom detection and tracking algorithms of a single ping and continuous pings are presented on the basis of the proposed model. The measured side-scan sonar data in Meizhou Bay and Bayuquan District were selected in the experiments to verify the model and methods. The 1D-UNet model was first trained and applied with the side-scan data in Meizhou Bay. The training and validation accuracies were 99.92% and 99.77%, respectively, and the sea bottom detection accuracy of the training survey line was 99.88%. The 1D-UNet model showed good robustness to the interference factors of bottom detection and fully real-time performance in comparison with other methods. Moreover, the trained 1D-UNet model is used to process the data in the Bayuquan District for proving model generality. The proposed 1D-UNet model for bottom detection has been proven effective for side-scan sonar data and also has great potentials in wider applications on other types of sonars.

Highlights

The trained 1D-UNet model is used to process the data in the Bayuquan District for proving model generality
Conventional side-scan sonars have been widely used in many underwater research fields, such as underwater resource exploration, benthic habitat mapping, environmental investigation, seabed target detection, underwater rescue, and archaeology, because of certain advantages like low price and easy installation [1,2,3,4,5]
The conventional side-scan sonar is towed under the water and continuously records the backscatter strength data after the sound waves are projected from the transducer on the port and starboard side

Summary

Introduction

Conventional side-scan sonars have been widely used in many underwater research fields, such as underwater resource exploration, benthic habitat mapping, environmental investigation, seabed target detection, underwater rescue, and archaeology, because of certain advantages like low price and easy installation [1,2,3,4,5]. The sonar image constructed using these backscatter strengths can reflect the important information on the seabed, which enables the wide applications for side-scan sonars. Side-scan sonars can provide the backscatter information from the seabed to construct benthic habitat maps to help protect coastal ocean ecosystems [6,7,8,9,10]. In underwater rescue and archaeology, side-scan sonars can help in detecting important underwater targets, such as human beings [14] and wrecks [15,16]. In the investigation of marine resources and the environment, side-scan sonar images can be used for resource searching and seabed sediment classification to understand the environmental changes of the seabed [17,18,19]

Results

Discussion

Conclusion