This PDF file contains the front matter associated with SPIE Proceedings Volume 12561, including the Title Page, Copyright information, Table of Contents, and Conference Committee listings.

Atmospheric transmittance can critically affect the accuracy of measuring infrared characteristics exhibited by targets. On the whole, the existing measurement of atmospheric transmittance has complied with the engineering calculation results of the MODTRAN software by applying several vital parameters (e.g., temperatures, air pressures and water-vapor content). In general, the error of such a method exceeds 20%, and it is significantly impacted by local weather. In this study, a ratio correction method was adopted to decrease the error in measuring atmospheric transmittance. The correction factor was determined by comparing the directly measured value from the infrared images of reference blackbody at different temperatures with the calculated value of the MODTRAN. Subsequently, the correction factor could be exploited to correct atmospheric transmittance. The experiment for measuring infrared radiation was performed, and the radiance inversion error was reduced by more than 10% after the correction of atmospheric transmittance. Furthermore, the correction factor calculated from LWIR images could be extrapolated to other bands. Besides, the inversion accuracy of the infrared radiation characteristics significantly increased. Thus, the multi-band applicability of the correction method was verified.

Underwater wireless optical communication (UWOC) is suitable to be used in deep-sea, because the water is clear and the background is darkness. Recently, light emitting diode (LED) are preferred in such communication, because it has up to 120°divergence angle, which makes establishing optical link easier. To enlarge the output power of transmitter, there are several discrete LED chips in the light source usually. However, transmitter with more discrete LEDs is disadvantageous to reduce luminous surface, which is related with UWOC equipment’s resistance to water pressure. In this paper, a method is presented to reduce such influence by using integrated LED arrays. Firstly, an integrated LED array is designed with seven blue chips series-connectedly. Then, a compact transmitter is developed with four such arrays. The test results show that the transmitter has up to 20Mbps modulation rate with 5.38W optical power. Using this transmitter, it is hopeful to realize 20Mbps speed and more than 100m distance with a suitable high sensitivity receiver in deep-sea. The performance of this transmitter is nearly the same with the ones in previous sea trials, while the effective luminous surface is reduced. These results demonstrate the effectiveness of transmitter with integrated LED arrays. Method in this paper is useful and pragmatic for deep-sea UWOC application.

Water surface roughness is important but challenging to measure parameter which reflects the energy transfer at the air-water interface. This paper presents a field experiment obtaining lake surface video images (Fig. 1). Also, it establishes the relationship between the aerodynamic roughness of the lake surface (or wind speed) and the characteristics of the lake surface video images based on texture features and fractal dimensions. This work is a preliminary study of sea surface roughness measurement. The texture features and fractal dimensions are calculated by using the methods of gray level co-occurrence matrix, gray level-gradient co-occurrence matrix, autocorrelation function, Tamura texture feature, fractional Brownian motion autocorrelation, box counting, improved box counting, gray statistical increment, gray statistical definition and area measurement. The empirical values of lake surface roughness are found from measured wind speed and an empirical relation. The correlations between lake surface roughness (or wind speed) and texture features (or fractal dimensions) are evaluated based on the data from the field experiment. Furthermore, three types of noises with different parameters are introduced to the lake surface video images. Then noise suppression performances of these methods are evaluated. The experiments have demonstrated that lake surface image roughness calculated by texture (or fractal) methods and empirical relation between wind speed and lake surface roughness is effective for analyzing lake surface roughness. The running time of various methods is calculated to analyze the possibility of real-time detection. Plans for further investigation of lake or sea surface roughness features are also discussed.

With the development of underwater resources and underwater exploration, there is an urgent need for high-precision, real-time, and three-dimensional observation of underwater detection methods. As one of the new technologies of underwater detection, underwater digital holographic technology can fill the gap in underwater high-precision three-dimensional observation and detection, so the research and development teams have successively carried out the research and development of underwater digital holographic systems. This paper briefly introduces the application and types of underwater digital holographic technology, and reviews the design and algorithm of different underwater digital holographic systems.

Particulate organic nitrogen (PON) refers to the nitrogen contained in biological or other debris particles, and it plays important roles in the ecosystem functions and biogeochemical processes of the marine biology. This study aimed to explore the feasibility of retrieving oceanic PON concentration from ocean color remote sensing data, determine the bio-optical proxy for satellite PON retrieval, and develop satellite oceanic PON retrieval models for the global ocean. In situ PON data collected over the global ocean and MODerate-resolution Imaging Spectroradiometer (MODIS) Aqua Level-3 products were used. Three different types of models were tested: 1) apparent optical properties (AOPs)-based models, 2) inherent optical properties (IOPs)-based models, and 3) biological properties-based models. Results indicated that ocean color remote sensing could be used for oceanic PON concentration retrieval in the global ocean, and AOPs-based models calibrated produced better fitting performance than the other two types. In further studies, PON models will be used to produce the global ocean PON concentration products, and explore oceanic PON spatiotemporal variations and the underlying driving forces.

Ocean engineering has recently given a lot of attention to a laser and vision-based underwater range technique. This technique involves the use of lasers to create spot patterns on an object's surface that are then captured on camera. The range might then be determined from the patterns by mapping the correlation between camera spot patterns and real distance. However, the laser paths create "beams" in the camera as a result of backscattering underwater. This impacts the ranging accuracy and makes it challenging to determine the spot position. The Mask R-CNN algorithm-based approach is proposed in this research as a solution to this issue. First, an underwater visual laser ranging system was constructed using a camera, four lasers, and deep learning training with the Mask R-CNN algorithm to recognize and segment the spots in the image. The link between it and the target's distance is then determined by mathematical fitting, using the perimeter of the region the light spot occupies as the geometric feature quantity. Finally, the measured pattern in the camera predicts the object's distance. The findings demonstrate that the measurement accuracy is at the centimeter level, which is beneficial and advantageous for precise underwater ranging.

In this paper, a factor reflecting the uniformity and the efficiency of grating beam splitting is introduced, moreover the scalar diffraction calculation method combined with the Gerchberg-Saxton iterative algorithm and the simulated annealing algorithm is proposed. Using this method, we iteratively obtain the optimal grating phase distribution by minimizing the factor, and a 16×4 beam splitting DOE with 86.3% efficiency and 92% uniformity is designed which has been successfully applied in 3-D imaging Lidar. The design method discussed in this paper provides new ideas for high-precision multi-beam Lidar and has better engineering application value.

As a new type of technology, the integrated system of underwater wireless optical communication and radar will play a huge role in realizing flexible and high-speed communication links between underwater vehicles, underwater monitoring points, and marine vessels. It plays an important role in wireless sensor networks, ocean exploration and detection. This paper proposes an integrated system of underwater wireless optical communication and radar, which integrates the functions of communication and radar in the same system. A time-slot synchronous clock recovery method is proposed to recover communication signals and achieve high-reliability communication; a high-precision target imaging algorithm based on the first photon is proposed to achieve high-precision radar imaging. The communication performance is verified by simulation, and the influence of radar imaging quality is verified by experiment. The results show that the system can not only achieve the function of single-photon wireless optical communication, but also achieve the high-quality target imaging of single-photon level.

The laser methane remote sensor based on tunable diode laser absorption spectroscopy (TDLAS) technology uses open optical path instead of traditional absorption chamber to achieve zero-contact long-distance gas detection. It has the advantages of high measurement accuracy, fast response speed and large detection range. It can be applied to gas leakage monitoring places such as stations of urban natural gas pipeline network, transportation pipelines, underground comprehensive pipe corridors and industrial oil and gas drilling and production safety fields. This paper mainly introduces two kinds of laser methane remote sensor based on TDLAS, which are fixed reflection laser methane telemetry and echo reflection laser methane telemetry.

Suspended particle is the main components of atmospheric aerosols, it is important to determine the composition, content, source, and concentration variation of suspended particles in the atmosphere. In this paper, we propose a new method based on the canonical correlation analysis(CCA) method to analyze the correlation between the polarized data and the concentration of suspended particles in the atmosphere. The new method can analyze very weak correlations between two sets of variables, which are both high-throughput and high-dimensional. Combined with the kernel probability density function, we can find out the elements that are more correlated in the two sets of variables. Firstly, the component of the suspended particles with the highest correlation between multi-angle polarized light data and suspended particles' concentration reference values are presented. Then, the concentration change of suspended particles in the atmosphere is predicted based on the measured polarized light data by the locally weighted linear regression(LWLR) method initially, the consistency of experimental data and predicted data verified the effectiveness of the new method.

Using a variety of observation data from satellite and BGC-Argo, this work investigates the influence of Typhoon Hagibis on ecological responses of the upper ocean in the Northwest Pacific Ocean. Measurements of chlorophyll-a concentration and sea surface temperature from Himawari-8 associated with Typhoon Hagibis were analyzed. The results show that the chlorophyll-a concentration along the typhoon track increased within 2-3 days after its transit, and the maximum value reached 0.3 mg/m³ or even higher while the sea surface temperature responded until 4-5 days later, with seawater cooling by 0.17-0.3°C. The observations of BGC-Argo demonstrate that Ekman pumping phenomenon may have occurred during the passage of Typhoon Hagibis, which induced the vertical transportation of deeper seawater to the upper and surface ocean, resulting in the increase of chlorophyll-a concentration, the decrease of temperature in mixed layer along the typhoon path.

In this paper, we propose a multi-scale residual attention network (MSR-Net) segmentation algorithm, which uses the ResNet50 residual network as the backbone feature extraction network and introduces a multi-scale channel attention mechanism. The MSR-Net uses the ResNet50 residual network as the backbone feature extraction network and introduces a multi-scale channel attention mechanism, which enables the network model to retain more complete sample edge information, significantly improves the segmentation capability of the model and ensures its network performance, which can effectively meet the needs of underwater image segmentation-related tasks. The proposed network is tested on the DUT-USEG dataset, and the recall, accuracy and average cross-merge ratio are 74.17%, 83.21% and 65.96%, respectively. As shown by the experimental results, compared with the classical U-Net, PSPNet and DeepLabV3, the performance indexes of the method in this paper are significantly improved.

High-quality underwater images and videos play an important role for exploitation tasks in underwater environments, but the complexity of the underwater imaging environment makes the quality of the acquired underwater images generally low. In order to improve underwater image quality by enhancing the resolution of underwater images, we propose an underwater image super-resolution method based on the improvement of SRGAN. The images generated by conventional super-resolution methods lose details and high-frequency information, resulting in overly smooth images. We have improved SRGAN by replacing the original Residual Block with Residual Dense Block in the feature extraction network, which improves the network performance and speeds up the training and testing of the model. And the Shuffle Attention mechanism is incorporated after each residual block, which efficiently combines the channel attention mechanism and the spatial attention mechanism, significantly improving the network's ability to extract features and generating high-resolution images with richer detail information. At the same time, our method effectively solves the problem that the generator generates images that are too smooth and without grain details. Our method is compared with SRGAN and SRResnet methods in USR-248, UFO public underwater image dataset, and the experimental results demonstrate that our method generates super-resolution images with better image detail enhancement and higher PSNR and SSIM values.

To address the problem of low accuracy of underwater image segmentation images due to low resolution, low visibility, a wide variety of objects and insufficient illumination of underwater acquired images, we build a super-resolution underwater image segmentation network based on multi-scale fusion. We apply Resnet50 as the backbone feature extraction network to the U-shaped network architecture, which can well enhance the feature capture capability of the model, and apply the upsampling output of the feature extraction model combined with the super-resolution module to obtain a refined output. Compared with other networks, our model has significant segmentation capability on the SUIM underwater image segmentation dataset. The shallow feature information of the input image can be better restored and preserved, and the edge feature information of the image is better preserved with higher accuracy, and the model has higher cross-merge ratio and accuracy. Our work is applied to the SUIM underwater image segmentation dataset and compared with U-Net( vgg16),U-Net(Resnet50), PSPNet(Mobilenet), PSPNet(Resnet50), deeplab(mobilenet) networks on this dataset, and our proposed network The average cross-merge ratio (MIou) of our proposed networks improved by 4.29%, 1.58%, 7.44%, 2.69% and 3.24%, respectively, and the pixel accuracy (mPa) improved by 3.24%, 1.57%, 5.4%, 1.93% and 5.73%, respectively.