A Robust Sidescan Sonar Bottom-Tracking Method Based on an Adaptive Threshold

Bottom tracking is an essential step in sidescan sonar image processing, which plays a crucial role in geometric distortion correction, geocoding, and image stitching. However, it is difficult to achieve accurate and automatic bottom tracking due to the influence of reflected surface echoes or suspensions in water. Therefore, this article proposes a robust and automatic bottom-tracking method considering multiple influencing factors. First, the bottom-tracking range is delineated by determining whether there are surface echoes. Then, the intensity difference between adjacent sampling intervals of the sonar image is calculated and bottom tracking is performed by an adaptive threshold. Finally, the bottom-tracking results are smoothed by combining them with the robust linear regression algorithm. Experimental results show that the detection accuracy of the proposed method is above 95%, which is higher than the results of the conventional threshold method (79.3%), Laplacian of Gaussain (LOG) operator (77.0%), and Canny operator (87.0%). The proposed method can adaptively adjust the threshold parameters and has a better bottom-tracking result with less computational complexity.