J. Eur. Opt. Society-Rapid Publ. 22, 6( 2026) 53
distinguish. We also found the signals from the shallow water were so strong that the value was beyond the maximum the data acquisition unit could record. It was the reason that signals from the water surface to about 2 m depth were almost the same.
It is apparent to see the target’ s signals at a depth of 7.0 m in the simulated results of Figures 8c and 8d, whichisvery consistent with the measured results in Figures 8a and 8b. The simulated target’ s signals scanned in case 1 are stronger than in case 2. Other than the consistency of the target’ s position, the quantity of simulated photoelectrons was consistent with the order of magnitude of measured photons. All of the photoelectron numbers first decreased from 10 3 at the water surface to about 10 2. 5 at a depth of 3.0 m, then decreased to 10 1 at 7.0 m. The numbers of photoelectrons from the target were around 10 2 in case 1 and around 10 1. 5 in case 2. To sum up, the above comparisons demonstrated the correctness of the MCT model.
5 Analysis of the detection capability of SOL
Fig
. 7. Field experiment of SOL,( a) experimental area in Thousand-Island Lake,( b) the inherent optical properties of experimental water,( c) underwater placement of the target,( d) geometry of the SOL flying over the underwater target.
Attenuation Sensor ac-s( WET Labs Inc.), and the results are shown in Figure 7b. The green metal barrel was selected as the underwater target. Its length is 0.8 m and its radius is 0.3 m. Its surface has a strong reflection and the reflectivity is about 0.5. As shown in Figure 7c, the ball float and the clump weight were used to fix the metal barrel at a water depth of about 7 m. The lidar SOL was mounted on the UAV DJI Flycart 30. The FPV camera on the UAV can help monitor the surface and assist lidar detection. The direction of flight was perpendicular to the long side of the target( Fig. 7d). Case 1 and case 2 each represent a group of laser beams making up a single linear scanning.
The SOL measurements were processed through a series of corrections. In the data pre-processing phase, a lowcomplexity method based on linear approximation of the leading edge( LLE) was used to extract the water surface precisely, then the slope distances both in the air and water could be calculated as well [ 71, 72 ]. Next, according to the angle information of the rotating motor, the slope distances were converted to vertical distances. The one-dimensional signals could be put together as two-dimensional images. Finally, the intensity of the lidar’ s output was converted to the number of photoelectrons by calibrating the singlephoton response of the detector in the lab.
Figures 8a and 8b show two typical scanning images, and the relevant observed geometries of the UAV that flew over the target are shown in Figure 7d, marked as 1 and 2. Figure 8a was obtained near the top of the target center( case 1), while Figure 8b was obtained from the top edge of the target( case 2). Both of the results had strong signals at a water depth of about 7 m, which revealed the good detection capabilities of SOL. The intensity of the target signals in case 2 was declining and became difficult to
Using the MCT model, the detection capabilities of SOL are further explored. The simulation experiments are carried out under two typical kinds of seawater: Jerlov II and Jerlov 3C [ 73, 74 ]. IOPs shown in Figures 9a and 9b are from WOOD( World-wide Ocean Optics Database). The lidar system parameters and the size of the target are the same as in Section 4.2.
To evaluate the detection differences between two kinds of water, we simulate the signals generated by the target placed at different z-coordinates in case 1, as shown in Figures 10 and 11. The scanning horizontal resolution is 0.4 m. The simulated results show that, when the depth of the target is smaller than 25.0 m in Jerlov II water and 9.0 m in Jerlov 3C water, it is easy to recognize the target. When the z-coordinate of the target is 30.0 m in Jerlov II water and 11.0 m in Jerlov 3C water, the target is barely possible to recognize. Besides, the thickness of the target’ s signal, which means its depth range, is greater in Jerlov 3C. This might be relevant to the multiple scattering.
Generally speaking, the target recognition of a twodimensional image is more difficult than that of a single profile. This is because in a two-dimensional image, there must be continuous target features( at least two continuous profiles) to be convincing. As shown in Figures 10 and 11, the strongest target signals exist near the center of it( x = 0), and decrease with horizontal distance from the center. Based on the results of Figures 10 and 11, we simulate the detection capability of a single profile of multiple z-coordinates of the target in case 1. We use N st �N sw to acquire the signals that are only reflected by the target. The feature peaks of N st �N sw signals with different depths are extracted and plotted as Figure 12a. The signal-to-noise ratios of these feature peaks are calculated and shown as Figure 12b. It can be seen that the photoelectron numbers in both water environments go down exponentially with depth, and in Jerlov 3C water, they decrease much faster because of the bigger attenuation coefficients. The same trend appears in the distribution of target signal SNR with respect to depth, as shown in Figure 12b.