Propagation Of Light In Water Research Articles

Oceanic turbulence parameters recognition based on convolutional neural network

The distortion induced by ocean turbulence has a substantial impact on the propagation of light in water, posing challenges for applications including underwater wireless optical communications and submarine surveys. Obtaining accurate information about the properties of oceanic turbulence (OT), particularly the parameters describing OT, is crucial for addressing these challenges and enhancing the performance of such applications. In this paper, we propose a convolutional neural network (CNN) and validate its ability to recognize OT parameters. The physical quantities of oceanic turbulence collectively influence the formation and strength of turbulence. We recognize the dissipation rate of temperature variance χ T and the turbulent kinetic energy dissipation rate ɛ, taking into account various balance parameter ω, transmission distance z. Furthermore, in order to simultaneously recognize χ T and ɛ, we enhanced the existing network by modifying the output structure, resulting in a dual-output architecture that facilitates concurrent classification of both χ T and ɛ. Our method for classifying turbulence parameters will contribute to the field of underwater wireless optical communication and promote its further development.

Sentinel-3 OLCI observations of Chinese lake turbidity using machine learning algorithms

Turbidity has a substantial effect on light propagation in water and is an optical indicator for monitoring water quality. The Ocean and Land Colour Instrument (OLCI) installed in the Sentinel-3 satellite could help to monitor turbidity variations due to its narrow spectral bandwidth and high revisit time. This makes us develop suitable turbidity algorithms for mapping the dynamics of lake turbidity using OLCI imagery. In this study, a total of 1081 samples were collected from 73 typical lakes across China during 2017–2022. Here, we developed and proposed applicable turbidity algorithms i.e., SR (simple regression), PLSR (partial least squares regression), SVR (support vector regression), BP (backpropagation neural network), KNN (K-nearest neighbor), RF (random forest), and XGBoost (extreme gradient boosting), which integrate a broad scale dataset of turbidity ranging from 0.15 NTU to 262.57 NTU using OLCI imagery. The performance results of developed turbidity algorithms demonstrated that RF obtained performed best with good agreements of measured- and derived- fitting (validation dataset: R2 = 0.92, MAPE = 54.32%, RMSE = 12.65 NTU). This allowed us to estimate turbidity concentration in Chinese lakes with a surface area>20 km2. The averaged turbidity level of lake groups in five lake regions was investigated with a decreased tendency as Northeast Plain Lake Region (NPL, 63.31 NTU) > Eastern Plain Lake Region (EPL, 58.30 NTU) > Inner Mongolia–Xinjiang Lake Region (IMXL, 16.64 NTU) > Yunnan–Guizhou Plateau Lake Region (YGPL, 11.63 NTU) > Tibetan Plateau Lake Region (TPL, 7.05 NTU). Further, we analyze the distributions of lake turbidity considering different land use types, and the results showed a decreasing tendency as cropland (56.97 NTU) > barren land (11.22 NTU) > grassland (8.06 NTU) > forest (4.71 NTU). Our results demonstrated and highlighted the quantification of lake turbidity concentrations using OLCI imagery and the stability of the RF algorithm, which can generate turbidity data for large-scale monitoring and decision-making related to environmental protection.

The aquatic particle number quandary

Optical surveys of aquatic particles and their particle size spectra have become important tools in studies of light propagation in water, classification of water masses, and the dynamics of trophic interactions affecting particle aggregation and flux. Here, we demonstrate that typical settings used in image analysis vastly underestimate particle numbers due to the particle – gel continuum. Applying a wide range of threshold values to change the sensitivity of our detection system, we show that macrogels cannot be separated from more dense particles, and that a true particle number per volume cannot be ascertained; only relative numbers in relation to a defined threshold value can be reported. A quandary thus presents itself between choosing a detection threshold low enough to accurately record orders of magnitude more particles on one hand or selecting a higher threshold to yield better image quality of plankton on the other. By observing the dynamics of coagulation and dissolution steps unique to cation-bridged gels abundant in aquatic systems, we find naturally occurring gels, and microscopic particles attached to them, to cause the ill-defined particle numbers. In contrast, the slopes in particle number spectra remained largely unaffected by varying sensitivity settings of the image analysis. The inclusion of fainter particles that are not typically captured by imaging systems provides a window into the true microscale spatial heterogeneity at scales relevant to small plankton organisms and processes that are dependent on particle density such as surface-associated chemical reactions as well as particle coagulation and aggregation dynamics.

YOLO-Submarine Cable: An Improved YOLO-V3 Network for Object Detection on Submarine Cable Images

Due to the strain on land resources, marine energy development is expanding, in which the submarine cable occupies an important position. Therefore, periodic inspections of submarine cables are required. Submarine cable inspection is typically performed using underwater vehicles equipped with cameras. However, the motion of the underwater vehicle body, the dim light underwater, and the property of light propagation in water lead to problems such as the blurring of submarine cable images, the lack of information on the position and characteristics of the submarine cable, and the blue–green color of the images. Furthermore, the submarine cable occupies a significant portion of the image as a linear entity. In this paper, we propose an improved YOLO-SC (YOLO-Submarine Cable) detection method based on the YOLO-V3 algorithm, build a testing environment for submarine cables, and create a submarine cable image dataset. The YOLO-SC network adds skip connections to feature extraction to make the position information of submarine cables more accurate, a top-down downsampling structure in multi-scale special fusion to reduce the network computation and broaden the network perceptual field, and lightweight processing in the prediction network to accelerate the network detection. Under laboratory conditions, we illustrate the effectiveness of these modifications through ablation studies. Compared to other algorithms, the average detection accuracy of the YOLO-SC model is increased by up to 4.2%, and the average detection speed is decreased by up to 1.616 s. The experiments demonstrate that the YOLO-SC model proposed in this paper has a positive impact on the detection of submarine cables.

New Monte Carlo Integration Models for Underwater Wireless Optical Communication

Underwater wireless optical communications are knowing a high interest in recent years, with many military and civil applications. The design of efficient systems relies on powerful simulation tools. The most used simulation algorithms are based on a paper from 1989, written by Prahl and designed for light propagation through human tissue. This method relies on Monte Carlo Simulation, following the path of a photon in the participating media. As such, it is difficult to propose evolution or optimization. In addition, a large amount of photons are needed to obtain good results with low error, <i>e.g</i>. for turbid water. With Monte Carlo Integration method, there exist a lot of optimization techniques to reduce the variance. This paper proposes a mathematical formalization of the propagation of light in water or any other participating medium as an integral problem. Therefore, it opens the way for a large number of future optimizations. A very straightforward and simple variance reduction technique is proposed as an example. Our simulation results show that this new technique has a lower sample variance as expected, and thus better convergence rates.

Color correction and restoration based on multi-scale recursive network for underwater optical image

Underwater image processing has played an important role in various fields such as submarine terrain scanning, submarine communication cable laying, underwater vehicles, underwater search and rescue. However, there are many difficulties in the process of acquiring underwater images. Specifically, the water body will selectively absorb part of the light when light travels through the water, resulting in color degradation of underwater images. At the same time, due to the influence of floating substances in the water, the light has a certain degree of scattering, which will bring serious problems such as blurred details and low contrast to underwater images. Therefore, using image processing technology to restore the real appearance of underwater images has a high practical value. In order to solve the above problems, we combine the color correction method with the deblurring network to improve the quality of underwater images in this paper. Firstly, aiming at the problem of insufficient number and diversity of underwater image samples, a network combined with depth image reconstruction and underwater image generation is proposed to simulate underwater images based on the style transfer method. Secondly, for the problem of color distortion, we propose a dynamic threshold color correction method based on image global information combined with the loss law of light propagation in water. Finally, in order to solve the problem of image blurring caused by scattering and further improve the overall image clarity, the color-corrected image is reconstructed by a multi-scale recursive convolutional neural network. Experiment results show that we can obtain images closer to underwater style with shorter training time. Compared with several latest underwater image processing methods, the proposed method has obvious advantages in multiple underwater scenes. Simultaneously, we can restore the color information, remove blurring and boost detail for underwater images.

Experimental and simulation assessments of underwater light propagation

This paper investigates the light propagation through several types of water by experimental and simulation. The Zemax-ray tracing software allowed to simulate the propagation of light in water and to observe the receiver response by reproducing the real conditions of propagation. The underwater environment has been reproduced by a 1.2 m long water tube and 20 cm in diameter with a glass window fitted on one side. The use of tap water with different amounts of sand leads toward three types of water with different attenuation coefficients (0.133, 0.343, 0.580 m−1). The light transmission in the three types of water was experimentally evaluated using a doubled Nd:YAG laser with energy of 4.3 mJ and a pulse width of 20 ns. Comparisons were done between simulation and experimental results.

TuLUMIS - a tunable LED-based underwater multispectral imaging system

Multispectral imaging (MSI) is widely used in terrestrial applications to help increase the discriminability between objects of interest. While MSI has shown potential for underwater geological and biological surveys, it is thus far rarely applied underwater. This is primarily due to the fact light propagation in water is subject to wavelength dependent attenuation and tough working conditions in the deep ocean. In this paper, a novel underwater MSI system based on a tunable light source is presented which employs a monochrome still image camera with flashing, pressure neutral color LEDs. Laboratory experiments and field tests were performed. Results from the lab experiments show an improvement of 76.66% on discriminating colors on a checkerboard by using the proposed imaging system over the use of an RGB camera. The field tests provided in situ MSI observations of pelagic fauna, and showed the first evidence that the system is capable of acquiring useful imagery under real marine conditions.

基于GOCI影像的湖泊悬浮物浓度分类反演

PDF HTML阅读 XML下载 导出引用 引用提醒 基于GOCI影像的湖泊悬浮物浓度分类反演 DOI: 10.5846/stxb201411152264 作者: 作者单位: 南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室,南京师范大学虚拟地理环境教育部重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目(41171269,41471282) Method for estimating the concentration of total suspended matter in lakes based on goci images using a classification system Author: Affiliation: Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University,Key Laboratory of Virtual Geographic Environment,Ministry of Education,College of Geographic Science,Nanjing Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:悬浮物直接影响到光在水体中的传播,进而影响着水生生态环境,最终决定了湖泊的初级生产力。传统的遥感反演估算模型大多是针对某一湖区进行统一建模,忽视了不同区域水体光学性质的复杂差异性,并且传统的传感器时间分辨率和空间分辨率受到一定限制。针对太湖、巢湖、滇池、洞庭湖4个湖区利用两步聚类法将高光谱模拟到GOCI影像上的波段进行分类,将水体类型分为三类,第一类水体为悬浮物主导的水体,第二类水体为悬浮物和叶绿素a共同主导的水体,第三类水体为叶绿素a主导的水体。针对不同类型水体的光学特征,分别构建了悬浮物浓度反演模型,结果表明第一类水体可以利用B7/B4,第二和第三类水体可以利用B7/(B8+B4)作为波段组合因子对悬浮物浓度进行模型构建。精度验证结果表明,分类建模后第一类和第三类水体悬浮物浓度估算精度都得到了较明显提高,第一类水体RMSE降低了9.19mg/L,MAPE降低了3%,第三类水体RMSE降低了5.63 mg/L,MAPE降低了13.97%,第二类水体精度稍有降低。最后将反演模型应用于2013年5月13日的GOCI影像,可知整体而言太湖西南部地区悬浮物浓度较高,东北部地区悬浮物浓度较低,并且从9:00到15:00,太湖南部悬浮物浓度较高的区域在逐渐缩小。 Abstract:Total suspended matter (TSM) is an important water quality indicator that can directly affect the propagation of light in water and influence the aquatic ecological environment, and ultimately determines the primary productivity of a lake. Empirical TSM concentration estimation models are often built for specific study areas, ignoring variation in the optical properties of water among diverse areas. In addition, common satellite sensors cannot be successfully used to monitor inland lakes owing to their temporal and spatial resolution. Taihu Lake, Chaohu Lake, Dianchi Lake, and Dongting Lake were selected as our study lakes, and an automatic two-step cluster method was applied for water classification based on simulated geostationary ocean color imager (GOCI) reflectance spectra. The results showed that the water samples could be classified into three types. The optical features of Water Type 1 were influenced by the TSM, the optical characteristics of Water Type 2 were influenced by both TSM and chlorophyll-a (Chl-a), and the optical properties of Water Type 3 were mainly determined by Chl-a. Estimation models were then developed for each water type using a band ratio of B7/B4 for Water Type 1 and B7/(B8 + B4) for Water Types 2 and 3 to retrieve the concentration of suspended solids. The root mean-squared errors (RMSEs) and minimum absolute percentage errors (MAPEs) of Water Type 1 were 9.19 mg/L and 3%, and those of Water Type 3 were 5.63 mg/L and 13.97%, respectively, which were significantly lower than those estimated using methods that do not consider this classification. The RMSE and MAPE of Water Type 2 were slightly higher than those estimated with the general algorithm. The diurnal variation of the TSM concentration in Taihu Lake was studied based on the GOCI data acquired on May 13, 2013 using this classification method, and the results showed that the concentration of TSM was higher in the southwest than in the northeast. In addition, the area of higher TSM concentration in the southern region of the lake was reduced from 9:00 to 15:00(Beijing Local Time). 参考文献 相似文献 引证文献

Investigation of Vision-Based Underwater Object Detection with Multiple Datasets

In this paper, we investigate the potential of vision-based object detection algorithms in underwater environments using several datasets to highlight the issues arising in different scenarios. Underwater computer vision has to cope with distortion and attenuation due to light propagation in water, and with challenging operating conditions. Scene segmentation and shape recognition in a single image must be carefully designed to achieve robust object detection and to facilitate object pose estimation. We describe a novel multi-feature object detection algorithm conceived to find human-made artefacts lying on the seabed. The proposed method searches for a target object according to a few general criteria that are robust to the underwater context, such as salient colour uniformity and sharp contours. We assess the performance of the proposed algorithm across different underwater datasets. The datasets have been obtained using stereo cameras of different quality, and diverge for the target object type and colour, acquisition depth and conditions. The effectiveness of the proposed approach has been experimentally demonstrated. Finally, object detection is discussed in connection with the simple colour-based segmentation and with the difficulty of tri-dimensional processing on noisy data.

Investigation of the effect of scattering agent and scattering albedo on modulated light propagation in water

A recent paper described experiments completed to study the effect of scattering on the propagation of modulated light in laboratory tank water [Appl. Opt.48, 2607 (2009)APOPAI0003-693510.1364/AO.48.002607]. Those measurements were limited to a specific scattering agent (Maalox antacid) with a fixed scattering albedo (0.95). The purpose of this paper is to study the effects of different scattering agents and scattering albedos on modulated light propagation in water. The results show that the scattering albedo affects the number of attenuation lengths that the modulated optical signal propagates without distortion, while the type of scattering agent affects the degree to which the modulation is distorted with increasing attenuation length.

Computer modeling and the design of optimal underwater imaging systems

A computer model to simulate the formation of underwater images has been developed. The model incorporates the inherent and apparent properties of the propagation of light in water. An image is approximated as a linear superposition of several image components. The model has been used to simulate the relative advantages of different camera/light configurations. The results indicate that extremely large gains in image contrast can be obtained by careful design of beam patterns and the manipulation of camera and light locations. The performance of range-gated systems is explored, and it is demonstrated that these systems are presently power limited. In order to obtain better quality images at larger distances, an imaging configuration which consists of scanning an incoherent light beam across the field of view of a camera is proposed. The incoherent light-scanning system is shown to have advantages over both conventional imaging techniques and range-gated methods. >

XLI. Investigation of the alteration produced by heat in the velocity of propagation of light in water

XLI. <i>Investigation of the alteration produced by heat in the velocity of propagation of light in water</i>

XXX. Investigation of the alteration produced by heat in the velocity of propagation of light in water

(1868). XXX. Investigation of the alteration produced by heat in the velocity of propagation of light in water. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science: Vol. 35, No. 237, pp. 262-281.