3.2.1. Validation on a Larger Survey Line

The side scan data of a long survey line with 13,504 pings were used for the validation. The survey line spanned the seabed of two different sediments, which appeared as the clearly lighter and darker areas in the side scan image, as shown in Figure 13b. At the joint area of the two sediments, the seabed topography rapidly changed, as shown in Figure 13c. Based on the 1D-CNN model that was trained by using the sample set of a small survey line, the bottom tracking of the validation survey line was processed, and the results are shown in Figure 13. During the processing, the search ranges of each ping were self-adapted to improve the speed, according to Table 1.

**Figure 13.** Bottom tracking of a larger survey line. (**a**) This area shows the port and starboard bottom tracking results, (**b**) this area shows the bottom tracking results represented in the side scan waterfall image, and (**c**) this part shows the seabed area where the terrain changes rapidly.

As shown in Figure 13a, the port and starboard results were consistent with each other, and the bottom tracking lines coincided with the edges of the port and starboard seabed in the waterfall image shown in Figure 13b. After removing the missing ping data of this survey line, the accuracy of the bottom tracking results reached 99.5%. In the area where the seabed terrain changed rapidly, the proposed bottom tracking method with auto-adapt search ranges still achieved good tracking results, which proved the validity of the proposed method in both flat and rugged seabed environments.

#### 3.2.2. Comparison with Other Bottom Tracking Methods

To compare the proposed method with traditional methods, the survey line was processed by using the last peak method [19]. For real-time processing, the last peak method was used without post-processing, including Kalman filtering. The tracking results obtained by both methods are shown in Figure 14.

**Figure 14.** Bottom tracking results obtained by using the last peak method and 1D-CNN.

The bottom tracking results obtained by using these two methods were consistent in most positions. However, the results obtained by the traditional method based on the numeric features were sensitive to noise, such as water column noise and seabed objects., so the results could possibly be inaccurate without post-processing. As for the proposed method, by training the sample sets, the network could properly learn the variation feature of the backscatter strength sequences, and show better robustness to noise. As more samples were learned, the 1D-CNN could more accurately recognize the side scan data. The comparison proved the validity and performance of the proposed method.

#### 3.2.3. Comparison Between the Bottom Tracking Depths and Ground Truth (Manual Annotations)

The manual annotations of bottom positions were used as the ground truth for the bottom tracking results of the side scan sonar data. Additionally, the bathymetric data measured by the multibeam sonar can be regarded as good references for the bottom tracking results. The depth of the side scan sonar sensor can be obtained by using its depth sensor, and the sonar height can be calculated by using the bottom tracking results. Therefore, the depth *D* of the corresponding seabed can be calculated using the equation below.

$$D = \frac{n \times t \times v}{2} + d \tag{7}$$

where *n* is the *n*th sample detected as bottom, *t* is the sampling interval time, *v* is the sound velocity, and *d* is the side scan sonar depth.

The digital elevation model was constructed by using the multibeam bathymetric data in the selected survey marine area (Figure 13c), as shown in Figure 15a. The track line of the multibeam data was extracted, and the corresponding water depths are shown in Figure 15d. The seabed depths of the side scan survey line that corresponded to the multibeam survey line were calculated by using the manual annotation and the predicted data, as shown in Figure 15b,c.

In the same water area, the seabed depths measured by the multibeam sonar (Figure 15d), those calculated by using manual annotations (Figure 15b), and the bottom tracking results (Figure 15c) were consistent with each other. The significant terrain fluctuations in the middle of the region coincided with the seabed variation shown in Figure 13c. Given that the multibeam and side scan data were measured at different times and that the multibeam data were not post-processed, the depths of the multibeam data had some errors and showed slight deviations from the depths calculated by using the side scan data. The terrain variation trends were consistent with each other, which proves the accuracy of the bottom tracking data.

**Figure 15.** Depths comparison between the manual annotations, bottom tracking results, and multibeam bathymetric data. (**a**) This area shows the local seabed terrain, (**b**–**d**) show the depth sequences tracked by the manual annotations, predicted by the side scan data, and measured by using the multibeam sonar, respectively, and (**e**) shows the histogram and normal fitting (with the mean μ as 1.21 cm and standard deviation σ as 8.57 cm) of the depth errors between the predicted (**c**) and manual annotated (**b**) depths in centimeters.

The depth errors between the predicted and manual annotated depths were fitted using a normal curve with the mean μ equal to 1.21 cm and standard deviation σ equal to 8.57 cm, as shown in Figure 15e. Given that the errors of manual annotations were within ±3 samples (corresponding to ±12.0 cm), the depth errors were less than two times the error (i.e., 24.0 cm) can be acceptable. By statistical analysis, the depth errors (Figure 15e) within ±24.0 cm are in a 99.44% proportion. Thereby, the accuracy of the bottom tracking results compared with manual annotations is 99.44%.

#### 3.2.4. Real-Time Experiment

To verify the real-time performance of the proposed method, the spend times of each ping were recorded during the bottom tracking experiment. The bottom tracking results and time sequences are shown in Figure 16a,b, respectively, whereas the spend times were statistically analyzed to evaluate the real-time performance, as shown in Figure 16c.

**Figure 16.** Real-time experimental results obtained by using AMD R5-2600X and GTX-2070. The necessary memory to run the algorithm should not be less that 2GB and the graphic memory should not be less than 8GB. (**a**) This area shows the bottom tracking results of the line, (**b**) this area shows the corresponding spend times of each ping, and (**c**) this area shows the normal fit of times and its 99.9% confidence bound at 150 ms.

Given the auto-adapt search ranges used in the bottom tracking experiment, the spend times of each ping changed along with the variation rate of the seabed terrain. The spend times of each ping were fitted by using the normal distribution curve with a mean μ of 82.1 cm and a variance σ of 0.12 cm. According to the statistical analysis results, the confidence bound of the side scan sampling interval time of each ping (150 ms) was 99.9%, which suggests a 99.9% possibility for the calculation time of each ping to be shorter than the sampling interval time. Moreover, it is guaranteed that, given the number of predicted sample sequences being less than 60, the calculation speed is always less than 150 ms, where 150 ms is the interval time between two pings. The statistical results proved the real-time feasibility of the proposed method.

Moreover, if the prior depth range is known, then the search range of each ping would be smaller. Moreover, with better hardware and multi-thread computing, the calculation speed would be improved, as discussed in Section 4.4.

### *3.3. Bottom Tracking of Side Scan Data with Noise and Rich Texture*

To obtain the bottom tracking results of the other survey lines in the experimental area, data augmentation was applied on the sample sets as more survey lines were processed, as shown in Figure 8. The characteristic side scan data with large noise, rich seabed texture, and artificial targets were carefully processed and analyzed, as shown in Figure 17. The recorded side scan data contained missing pings, which had no backscatter strengths or very low backscatter strengths, as shown in yellow rectangles in Figure 17.

**Figure 17.** Bottom tracking of the characteristic side scan data with noise (**a**) and rich seabed texture (**b**) and artificial targets (**c**). The gaps shown in yellow rectangles between the pings are the missing data.

Figure 17a shows that the noises in the water column are relatively large. In the red rectangular area, the noises in the water column made the seabed and water column data indistinguishable, or made the edge variation of the seabed abnormal. The bottom tracking accuracy of this survey line

as obtained by 1D-CNN was 97.3% with a 2.0% miss-ping rate. The accuracy excluding the missing pings was 99.3%.

Figure 17b shows that the seabed has rich textures and that some noise can be observed in the water column. The backscatter strength variation of the complex seabed texture would result in clear light and shade areas, which would interfere with bottom tracking. The bottom tracking accuracy of this survey line as achieved by 1D-CNN was 93.1% with a 6.1% miss-ping rate. The accuracy excluding the missing pings was 99.1%.

Figure 17c shows that the seabed contains artificial targets, such as submarine pipelines. These artificial targets can also cause light and shade areas in the side scan image, which would significantly affect bottom tracking. The bottom tracking accuracy of this survey line as achieved by 1D-CNN was 94.5% with a 4.9% miss-ping rate. The accuracy excluding the missing pings was 99.4%, as shown in Table 2.


**Table 2.** Bottom Tracking Accuracies of the Survey Lines Shown in Figure 17.

As shown in Table 2, by means of sample data augmentation, mutual inspection of the port and starboard results, and auto-adapt search ranges, the proposed method can guarantee the bottom tracking accuracy of the side scan data with large amounts of noise, a rich seabed texture, and artificial targets as well as simultaneously realize real-time calculation performance. The average bottom tracking accuracy of the overall testing survey lines as achieved by 1D-CNN was 94.7% with a 4.5% miss-ping rate. The tracking accuracy excluding the missing pings was 99.2%. The experiments proved that the proposed method has high robustness to noise, and can yield accurate results in complex seabed conditions.
