This PDF file contains the front matter associated with SPIE Proceedings Volume 13061, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

The characterization of algae biomass is essential for ensuring the health of an aquatic ecosystem. Algae overgrowth can be detrimental to the chemical composition of a habitat and affect the availability of safe drinking water. In-situ sensors are commonplace in ocean and water quality monitoring scenarios where the collection of field data using readily deployable, cost-effective sensors is required. For this purpose, the use of compact time domain nuclear magnetic resonance (TD-NMR) is proposed for the assessment of Magnetic Particle (MP) content in algae. A custom NMR system capable of rapidly acquiring relaxometric data is introduced, and the T2 relaxation curves of algae samples sourced from Lake Wateree in South Carolina are analyzed. A clear correlation between the relaxation rate and MP concentration of the samples is observed, and the viability of the proposed scheme for MP-based estimations concerning algae is discussed.

Uncertainties in radiance above the ocean surface are mostly determined by the skylight reflected from the air-water interface. Their accurate characterization is important for the accurate measurements of the water-leaving radiance as well as for the estimation of the impact of these uncertainties on the atmospheric correction of satellite and airborne ocean observations. Uncertainties are affected by the state of the ocean surface dependent on the wind speed and the corresponding reflection coefficient, which can be calculated based on Cox-Munk relationships. These uncertainties were estimated in the hyperspectral mode from shipborne measurements by the Hyperspectral Imager ULTRIS X20 (Cubert, Germany), with a 400-1000 nm wavelength range and a 410x410 pixel resolution. Measurements were taken during a VIIRS Cal/Val cruise in Hawaii area in a broad range of wind speeds 0-10 m/s and at viewing angles 20-60 degrees. In addition, airborne measurements from a helicopter at four different altitudes of 60, 150, 450, and 750 meters were carried out in different parts of Chesapeake Bay to establish a relationship between uncertainties and altitude. For these, a Teledyne DALSA M2450 polarized camera with a filter wheel containing several filters at different spectral bands was used together with the imager to characterize wave slope statistics and to determine uncertainties in measurements of the Stokes vector components and the degree of linear polarization (DoLP). Measurement uncertainties are further compared with simulations.

Underwater ROVs (Remotely Operated Vehicles) play a crucial role in subsea inspection, remote surveillance, and deep-water explorations. Typically, a surface operator controls the ROV based on its real-time camera data, which is first-person visual feedback. However, underwater ROVs’ onboard camera feed only offers a low-resolution and often noisy egocentric view - that is not very informative in deep water and adverse visual conditions. To address this, we introduce the “Eye On the Back (EOB)” technology to provide a third-person view for improved underwater ROV teleoperation. Integrating EOB views to teleoperation consoles facilitates interactive features with augmented visuals for the teleoperator as well as for enabling semi-autonomous behavior such as next-best-view planner and active ROV localization. We conduct a series of field experiments to validate this technology for remote ROV teleoperation in underwater cave exploration and subsea structure inspection tasks. We are currently developing an end-to-end portable solution that can be integrated into existing ROV platforms for general-purpose subsea telerobotics applications.

Developing ocean-going unmanned robotic systems has been a focus for the marine research community for many years. Compared with earlier manned submersibles, the current state-of-the-art Autonomous Underwater Vehicles (AUVs), tethered Remotely Operated Vehicles (ROVs) and Unmanned Surface Vehicles (USVs) augmented with the advancement in the sensor technology offer dramatic improvements in safety, cost, and efficiency, especially for deep water sensing operations. However, coastal zones such as estuaries and river deltas that are highly productive habitats supporting a variety of fish and wildlife may be challenging for the current suite of platforms. The complex geographical features in these regions, such as land barriers, icebergs and tidal currents, may hinder the movements of the aforementioned platforms. For this reason, a complementary sensing paradigm that employs waterproof unmanned aerial vehicles (UAVs) integrated with underwater sensors is proposed. The implementation of such concept – the Hybrid Aerial Underwater Robotic System (HAUCS) is presented. The development of one HAUCS platform, the coaxial waterproof drone, is discussed.

Naval Air Warfare Center Aircraft Division (NAWCAD) engineers and scientists recently completed initial laboratory and field testing of the Modulated Underwater Laser Imaging System (MULIS) prototype. This represents the culmination of years of collaboration between NAWCAD, industry, and academia partners to transition NAWCAD’s radar-encoded laser imaging technology out of the lab and into the field. This paper presents results from both initial laboratory and field tests of the MULIS prototype. Laboratory tests evaluated imaging performance in a variety of simulated water clarity conditions. MULIS was then integrated into a REMUS 600 Autonomous Underwater Vehicle (AUV) for a field test event in the Chesapeake Bay in the summer of 2023. Multiple successful missions were run over the course of the field test, obtaining 3D imagery of the submerged objects despite the challenging water clarity conditions in the Chesapeake Bay.

During a twelve day field test west of the continental shelf off the coast of Washington state, we conducted multiple environmental data collection flights in a 150 km by 150 km area. We operated a scanning lidar system optimized for ocean profiling collecting near surface atmospheric return signal, surface reflections and optical profiles to several optical depths. The along and across track spatial resolution was approximately 10 meters and the vertical resolution was approximately 0.1 meters. We also deployed ten single use temperature profiling buoys during the test. We will present comparisons of the spatial-temporal lidar data to the buoy data and other public source data, such as satellite derived k-diffuse and Argo float data. It is our expectation that the lidar data will reveal complex and changing vertical optical structures on sub-kilometer horizontal scales that are not adequately captured by other ocean sensing techniques.

Scattering effects in underwater environments significantly challenge optical perception. This paper introduces a foveating confocal bistatic LiDAR system, uniquely capable of adaptive targeting with its MEMS-modulated transmitter and receiver in turbid underwater conditions. By dynamically adjusting its receiver instantaneous field of view to areas of interest, it effectively increases depth sampling in complex and challenging underwater environments. Applying bistatic principles, separating transmitter and receiver, we allow robustness to scattering media effects. We demonstrate LIDAR results in an underwater laboratory tank setting.

We demonstrate optical ranging through turbid underwater medium using a structured beam. This beam consists of two Bessel modes, each carrying a pair of orbital angular momentum order and longitudinal wavenumber. As a result, the beam has a “petal-like” intensity profile with different rotation angles at different distances. The object’s distance (z) is retrieved by measuring the rotation angle of the petal-like profile of the back-reflected beam. We demonstrate ⪅ 20-mm ranging errors through scattering with extinction coefficient γ up to 9.4 m^-1 from z = 0 to 0.4 m. We further experimentally demonstrate the enhancement of ranging accuracy using multiple (⪆2) Bessel modes. With the number of modes increasing from two to eight, the average error decreases from approximately 16 mm to approximately 3 mm for a Υ of 5 m^-1. Moreover, we simulate both coarse- and fine-ranging by using two different structured beams. One beam has a slower rotating petal-like profile, leading to a 4X larger dynamic range for coarse ranging. A second beam has a faster rotating profile, resulting in higher accuracy for fine ranging. In our simulation, ⪅ 7-mm errors over a 2-m dynamic range are achieved under 𝛾 = 4 m^-1 .

Plastic pollution is an always-growing problem in earth’s oceans. In this paper, we propose an aerial method to detect marine plastic litter, which can be utilized on oil pollution control aircraft already in use in many parts of the globe. With this approach resources are saved, and emission are reduced, as no additional aircraft has to take off. To prevent the growing accumulate of plastic litter in our oceans, two major approaches are necessary. First, one has to detect and collect the plastic that has already reached the ocean. Second, sources of plastic litter have to be found to prevent more plastic from reaching the oceans. Both approaches can be targeted using sensors on airborne platforms. To achieve this, we propose a method for litter detection from aircraft using artificial intelligence on data gathered with sensors that are already in use. For oil pollution control multiple aircraft are already flying in different regions all over the world. Sensors used on these aircraft are partially adapted and utilized in a new way. The detection of plastic is performed using a high frequency, low resolution visual line sensor. If plastic is detected, a high-resolution camera system is targeted on the detected plastic using a gimbal. These high-resolution images are used for verification and classification purposes. In addition to the development of the method for plastic detection, results from intermediate field tests are presented.

One of the main challenges in underwater automatic target recognition is in the data scarcity of underwater sonar imagery. This challenge arises especially in data-driven approaches because of the limited training dataset and unknown environmental conditions before the mission. Transfer learning and synthetic data generation have been suggested as effective methods to overcome this challenge. However, the efficiency and effectiveness of synthetic data generation methods have been limited due to the difficulty from implementing complex acoustic imaging processes and data-driven model’s poor performance under domain shifts. In this paper, we present a novel approach to address this challenge by utilizing cycle-Generative Adversarial Networks (GAN) to generate synthetic sonar images to enhance the effectiveness of the training data set. Our method simplifies the process of synthetic data generation by leveraging cycle-GAN, which is a deep Convolutional Neural Network (CNN) for image-to-image translation using unpaired dataset. The cycle-GAN based generation model transfers camera images of ship and plane into realistic synthetic sonar images. Then, these generated synthetic images are used to augment the training data set for the classification model. In this work, the effectiveness of this approach is demonstrated through a series of experiments, showing improvements in classification accuracy. One advantage of the proposed approach is in the simplification of the synthetic data generation process while improving classification accuracy. Another advantage is that the ship and plane sonar image generation model is trained solely on seabed sonar images, which are relatively easy to obtain. This approach has the potential to greatly benefit the field of underwater sonar image classification by providing a more efficient solution for addressing data scarcity.

The deep neural network has found widespread application in object detection due to its high accuracy. However, its performance typically depends on the availability of a substantial volume of accurately labeled data. Several active learning approaches have been proposed to reduce the labeling dependency based on the confidence of the detector. Nevertheless, these approaches tend to exhibit biases toward high-performing classes, resulting in datasets that do not adequately represent the testing data. In this study, we introduce a comprehensive framework for active learning that considers both the uncertainty and the robustness of the detector, ensuring superior performance across all classes. The robustness-based score for active learning is calculated using the consistency between an image and its augmented version. Additionally, we leverage pseudo-labeling to mitigate potential distribution drift and enhance model performance. To address the challenge of setting the pseudo-labeling threshold, we introduce an adaptive threshold mechanism. This adaptability is crucial, as a fixed threshold can negatively impact performance, particularly for low-performing classes or during the initial stages of training. For our experiment, we employ the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21), comprising 130 fish species classes with 28,328 image samples. The results show that our model outperforms the state-of-the-art method and significantly reduces the annotation cost. Furthermore, we benchmark our model’s performance against a public dataset (PASCAL VOC07), showcasing its effectiveness in comparison to existing methods.

Fish species must be identified for stock assessments, ecosystem monitoring, production management, and the conservation of endangered species. Implementing algorithms for fish species detection in underwater settings like the Gulf of Mexico poses a formidable challenge. Active learning, a method that efficiently identifies informative samples for annotation while staying within a budget, has demonstrated its effectiveness in the context of object detection in recent times. In this study, we present an active detection model designed for fish species recognition in underwater environments. This model can be employed as an object detection system to effectively lower the expense associated with manual annotation. It uses epistemic uncertainty with Evidential Deep Learning (EDL) and proposes a novel module denoted as Model Evidence Head (MEH) for fish species detection in underwater environments. It employs Hierarchical Uncertainty Aggregation (HUA) to obtain the informativeness of an image. We conducted experiments using a fine-grained and extensive dataset of reef fish collected from the Gulf of Mexico, specifically the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate that an active detection framework achieves better detection performance on the SEAMAPD21 dataset demonstrating a favorable balance between performance and data efficiency for underwater fish species recognition.

Accurate recognition of multiple fish species is essential in marine ecology and fisheries. Precisely classifying and tracking these species enriches our comprehension of their movement patterns and empowers us to create precise maps of species-specific territories. Such profound insights are pivotal in conserving endangered species, promoting sustainable fishing practices, and preserving marine ecosystems’ overall health and equilibrium. To partially address these needs, we present a proposed model that combines YOLOv8 for object detection with ByteTrack for tracking. YOLOv8’s oriented bounding boxes help to improve object detection across angles, while ByteTrack’s robustness in various scenarios makes it ideal for real-time tracking. Experimental results using the SEAMAPD21 dataset show the model’s effectiveness, with YOLOv8n being the lightweight yet modestly accurate option, suitable for constrained environments. The study also identifies challenges in fish tracking, such as lighting variations and fish appearance changes, and proposes solutions for future research. Overall, the proposed model shows promising fish tracking and counting results, which is essential for monitoring marine life.

Within the scope of aquaculture farm operation and research, monitoring fish larvae offers pivotal data about the operational conditions of the farm. For example, hypoxia may induce abnormal movements. Currently, precise monitoring of these diminutive entities (1 mm in size) hinges on superior water clarity and specialized equipment. While green laser may be preferred for extended range underwater imaging, it is visible to the fish. Hence it will disturb fish and potentially damage their vision system. This is of particular concern at our facility at the Harbor Branch Oceanographic Institute (HBOI). To address these challenges, our research has adapted a Time-of-Flight (ToF) camera, equipped initially with a 50mm lens, into a microscopic imager using an IR laser. This setup was capable of detailed but narrow depth field imaging, suitable for clear water conditions. Recent advancements have included transitioning to a 25mm lens, enhancing the camera’s ability to capture wider images (approximately 20 pixels wide for fish eggs) and observe finer details in medium turbidity conditions, though with a reduced depth field of 5mm. This modification has shifted the camera’s utility towards observing very small living organisms (100-200 microns) and reduced its effectiveness in depth measurement in highly turbid waters. This adaptation ensures more precise tracking of fish larvae and offers a fish-eye-safe imaging process due to the use of IR light. The integration of machine learning techniques further refines the system’s ability to accurately identify fish larvae in varying water conditions. Our approach presents a balanced solution, combining affordability, improved accuracy, and mindful consideration of the fish’s welfare, contributing positively to the field of fish larvae tracking.

In the ocean, underwater currents are driven by various natural effects attributed to heat transfer through water. The movement of heat subsequently affects light propagation due to changes in the water’s refractive index leading to optical phase distortions. Applications implementing laser beams containing structured phase profiles are prone to being distorted by this underwater optical turbulence. Typical distortions of these beams can include beam wander, intensity and phase variations, and beam spreading that can limit their effectiveness for applications including free-space optical communication, imaging, or sensing. Experimental and theoretical studies have shown optical vortices, a form of structured light, propagate differently through optical turbulence compared with Gaussian beams. Changes in propagation are observed by varying the amount of Orbital Angular Momentum (OAM) a vortex beam carries that increases the beam size as OAM increases. This experimental study intends to fairly compare Laguerre-Gaussian (LG) beams to Gaussian beams after propagation through underwater turbulence by normalizing the initial beam size using the RMS radius. The metrics chosen are the mean scintillation, on-axis intensity, and intensity correlation. Results show the scintillation and on-axis intensity, when chosen at locations along the LG beam annuli, are similar for different LG beams. When the initial beam waist is normalized, the speckle field correlation width and peak correlation energy decreases as RMS radius increases. These results show that structured light is not independent of the effects of beam size and divergence, similar to Gaussian beams, to determine propagation effectiveness or robustness.

Accurate measurement of laser light phase after propagation through underwater optical turbulence is crucial for defense and commercial applications like underwater communications and sensing. Traditional phase-measuring methods, like Shack-Hartmann wavefront sensors, have limited effectiveness in strong optical turbulence. The Gerchberg-Saxton (GS) method utilizes synchronized intensity images in the image and Fourier planes and retrieves the phase through an iterative algorithm. We evaluate the Gerchberg-Saxton algorithm's accuracy for laser light propagation through simulated Kolmogorov turbulence and experimentally generated Rayleigh-Bénard (RB) natural convection. The results of the phase retrieved from the experimental data recorded in pupil and focal planes are compared with the phase measurements from a Shack-Hartmann sensor. We tested the efficacy of the Gerchberg-Saxton algorithm to estimate the phase of laser light upon propagation through underwater optical turbulence.

NIWC Pacific will present a novel, cost-effective method for in situ measurement and characterization of atmospheric turbulence, as quantified by the atmospheric seeing parameter, r0. The technique will leverage spatially encoded QR codes that are imaged using normal imaging optics. The presentation will cover the theory of the technique along with simulation and experimental results compared to a commercial turbulence measurement system.

Optical turbulence causes fluctuations in the refractive index along a propagation path, leading to severe distortions in laser beams, which in turn cause a reduced performance of electro-optical systems like directed energy weapons, imaging systems, and free space optical communication systems. We propose experimental characterization of the optical turbulence height profile in the maritime and littoral surface layer, leveraging the versatility and mobility of Unmanned Aerial Vehicles (UAVs) over a vertical path. Initial experiments include a stationary set up to measure laser beam intensity fluctuations at five points along a vertical down link of ~30 m length. Our research results have applications for optical communication and energy delivery system between airborne and surfaced platforms (submarine or ship) through the marine surface layer.

This study delves into the Extended Kalman Filter's (EKF) use in ocean science through a detailed bibliometric and text mining examination. Tracing its roots back to the original Kalman Filter from the 1960s, the EKF has become crucial for managing nonlinear dynamics, especially in oceanography. Our analysis, drawing from Scopus data covering 1980-2023, delivers an extensive overview of the EKF's growth, applications, and cross-disciplinary influence in this area. We employed sophisticated bibliometric methods, including Biblioshiny, and text mining approaches via VOSviewer to dissect trends, and thematic groupings in EKF-related ocean science research. The results demonstrate a steady increase in EKF applications, particularly in autonomous underwater vehicle navigation, forecasting ocean currents, and modeling marine ecosystems. The bibliometric findings show its broad interdisciplinary appeal, while the text analysis underscores the EKF's integration with cutting-edge computational techniques and its significance in burgeoning oceanographic technologies. The paper highlights the EKF's indispensable role in ocean science, reflecting its historical importance and versatility in addressing contemporary challenges in marine technology. The study not only sheds light on the EKF's historical and current uses but also suggests potential future directions for research and innovation. It aims to offer crucial insights to researchers, academicians, and policy makers, underlining the EKF's significance in the dynamic, ever-changing realm of ocean science.