Roughness Change Analysis of Sea Surface From Visible Images by Fractals

Pan, Hailang; Zhang, Weicheng; Jiang, Wen; Wang, Ping; Yang, Jianguo; Zhang, Xin

doi:10.1109/access.2020.2990161

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…As an approach, this survey aims to explore a forward statistical surface roughness modeling of ROM using a series of well-established spreading functions in conjunction with the Elfouhaily's two-scale WFS, hereinafter referred to as directional two-scale WFS, to synthesize composite ROM surface roughness under varying sea state conditions [114]- [116], which have yet to be reported [6], [15], [23], [25], [30], [43], [44], [49], [52], [55]- [59], [63], [65], [81], [99], [102], [108], [113]. To achieve this, the wavenumber-domain is employed to express ROM scattering characteristics, incorporating frequency-dependent components derived from time-evolving sea states [94].…”

Section: Introductionmentioning

confidence: 99%

Directional Wave Scattering Distribution Modes Analysis and Synthesis of Random Ocean Media Roughness for SAR Electromagnetic Interactions Using Feature Fusion in Dynamic Sea States: A Survey

Shahrezaei,

Kim

2024

IEEE Access

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Ocean waves have long been a research topic, and numerous formulas of the ocean wave spectrum have been widely developed to provide new prospects for ocean experiments and the advancement of radar probing. Nonetheless, the wave spectra developed by researchers fall short of the standards set by remote sensing specialists, mainly due to the limitation of capturing the surface roughness influenced by both short and long waves, prompting ongoing efforts to develop a model covering a diverse scale of wavenumbers in the absence of a generally recognized reference formula. In response, a standard two-scale formulation of wave frequency spectra (WFS) was introduced, where wave height and spectral peak period are determined by sea state. The proposed composite WFS holds the potential to incorporate directional spreading, contributing to the angular distribution of ocean wave energy in the form of directional WFS, making it applicable for ocean modeling. In an effort to investigate directionality effects, an array of well-established spreading functions, including cosine-squared, half-cosine 2s, parameterized half-cosine 2s, hyperbolic secant-squared, and composite structured functions, has been developed here for numerical modeling of random ocean media (ROM) surface roughness and synthesized for their spectral scattering distribution mode, encompassing scattering pattern, scattering orientation, and fractal roughness properties. Nonetheless, the generated ROM models, each varying due to inherent limitations in directional WFS formulation and numerical approximations, demonstrate indeterminacy and unpredictability in surface features, posing challenges for accurately synthesizing ROM roughness patterns. These challenges intensify under varying sea states and different directional WFS formulas, leading to a situation where no single synthesized composite ROM model consistently outperforms the others, rendering them imprecise frameworks for analyzing roughness patterns and investigating texture electromagnetic interactions within the realm of remote sensing. As an approach, a pattern-sensitive fusion method is proposed, employing a multi-scale transform domain (MTD) fusion scheme that leverages the learning potential of a deep super resolution network. The objective is to fuse the reconstructed ROM roughness models, generating an optimal roughness while maintaining their scattering pattern, scattering orientation, and dominant directionality, pivotal for texture consistency and, consequently, the backscattering properties from the synthetic aperture radar (SAR) viewpoint. To validate the reliability of ROM modeling and its roughness synthesis, including the texture fusion and raw data generation, a comprehensive objective quality assessment technique is utilized. These assessments demonstrate the complete consistency of the simulation results with the underlying spectral theory, highlighting their potential contribution to projects related to ocean radar probing and remote sensing.INDEX TERMS Directional wave frequency spe...

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Section: Introductionmentioning

confidence: 99%

Directional Wave Scattering Distribution Modes Analysis and Synthesis of Random Ocean Media Roughness for SAR Electromagnetic Interactions Using Feature Fusion in Dynamic Sea States: A Survey

Shahrezaei,

Kim

2024

IEEE Access

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“…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

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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.

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“…Currently, both theoretical methods and numerical simulations are being applied to the study of physical phenomena that arise in ocean surface modeling that are inherently 2D and cannot be observed using 1D approximations, specifically while dealing with ocean waves known as sea state conditions [35][36][39][40][41][42][43]. Hence, characterizing the stochastic scattering properties of such nondeterministic complex media with a given statistical distribution of concentration and normal absorption data is considered a complicated approach to model ocean roughness [24,42,[44][45].…”

Section: Introductionmentioning

confidence: 99%

A Novel SAR Fractal Roughness Modeling of Complex Random Polar Media and Textural Synthesis Based on a Numerical Scattering Distribution Function Processing

Shahrezaei

Kim

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Since 2007, he has been working at the Korea Polar Research Institute (KOPRI) using satellite and UAV remote sensing data for the purpose of Arctic and Antarctic research. He established a remote sensing group in KOPRI in 2016. He leads the Center for Remote Sensing and GIS (CORAG) as a director.The center consists of many specialized fields of satellite remote sensing, ocean color, SAR, passive microwave, optical remote sensing, and UAVs. The group mainly studies the cryosphere for climate change using remote sensing data. Notably, he is the first scientist to use KOMPSAT (Korea Multipurpose Satellite) for polar science. He is also actively involved in many international programs. Dr. Kim was a recipient of a Korea Prime Ministerial Citation for Excellence in Ocean Science in 2019.

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Roughness Change Analysis of Sea Surface From Visible Images by Fractals

Cited by 7 publications

References 32 publications

Directional Wave Scattering Distribution Modes Analysis and Synthesis of Random Ocean Media Roughness for SAR Electromagnetic Interactions Using Feature Fusion in Dynamic Sea States: A Survey

Directional Wave Scattering Distribution Modes Analysis and Synthesis of Random Ocean Media Roughness for SAR Electromagnetic Interactions Using Feature Fusion in Dynamic Sea States: A Survey

Lake surface roughness measurements from video images

A Novel SAR Fractal Roughness Modeling of Complex Random Polar Media and Textural Synthesis Based on a Numerical Scattering Distribution Function Processing

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