Changes in Sea Surface Roughness in Light Wind

Shumeyko, I.P.; Abramovich, A. Yu.; Burdyugov, V. M.

doi:10.1007/978-3-030-76328-2_37

Cited by 3 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the roughness is high, this roughness can be displayed on the optical image. 8,9 This paper improves and optimizes related methods 10,11 based on the existing research and optical imaging mechanism. This paper constructed a parametric model of lake surface roughness using algorithms of gray level co-occurrence matrix (GLCM), gray level-gradient co-occurrence matrix (GLGCM), autocorrelation function (ACF), Tamura texture feature (TTF), fractional Brownian motion autocorrelation (FBMC), box counting (BC), improved box counting (IBC), gray statistical increment (GSI), gray statistical definition (GSD) and area measurement (AM) (In Sec.…”

Section: Introductionmentioning

confidence: 99%

Lake surface roughness measurements from video images

Han¹,

Pan

Yang³

et al. 2023

AOPC 2022: Atmospheric and Environmental Optics

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Lake surface roughness measurements from video images

Han¹,

Pan

Yang³

et al. 2023

AOPC 2022: Atmospheric and Environmental Optics

View full text Add to dashboard Cite

show abstract

“…In this paper, the analysis is carried out on the basis of the analytical model representing the group structure [10]. The model describes a nonlinear wave process [11].…”

Section: Introductionmentioning

confidence: 99%

Modeling of the group structure of waves in the problems of the impact of waves on a ship

Zapevalov

2023

E3S Web Conf.

View full text Add to dashboard Cite

The variability of the group structure sea surface waves is analyzed by numerical simulation. The analysis is carried out on the basis of an analytical model that describes the group structure of nonlinear waves. The characteristics of the wave group are controlled by two parameters, the group height factor (GFH) and the group length factor (GLF). With a fixed wave energy, the maximum wave height is determined only by GFH and does not depend on the GLF. It is shown that if GFH varies from 0.1 to 0.9, then at a fixed wave energy, the maximum wave height in the group increases by one and a half times. As the GLF increases, the number of high-height waves successively affecting the ship increases.

show abstract