Numerical Simulation and Backscattering Characteristics of Freak Waves Based on JONSWAP Spectrum

Convolutional neural networks have been created as deep learning-based approaches to automatically analyze photographs of concrete surfaces for crack diagnosis applications. Although deep learning-based systems assert to have extremely high accuracy, they frequently overlook how difficult it is to acquire images. Complex lighting situations, shadows, the irrationality of crack forms and widths, imperfections, and concrete spall frequently have an influence on real-world photos. The focus of the published research and accessible shadow databases is on photographs shot in controlled laboratory settings. In this research, we investigate the challenging underwater optical effects settings and the complexity of image classification for concrete crack detection. This research elaborates on difficulties encountered when using deep learning-based techniques to identify concrete cracks when optical effects are present. To improve the precision of automatically detecting concrete cracks on underwater surfaces, new optical effect augmentation techniques have been developed.

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“…This article employs the directional extension feature suggested by the stereo wave observation project (SWOP) [15].…”

Section: Mathematical Models For the Sea Surfacementioning

confidence: 99%

A deep learning-based approach for automatic detection of concrete cracks below the waterline

Orinaitė¹,

Palevicius²,

Pal³

et al. 2022

Vib. proced.

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“…Thus, conducting a survey on modeling of ROM using two-scale WFS has the potential to offer valuable insight into the physical and structural properties of the ocean surface, with implications for various ocean remote sensing interpretations [25], [26], [43], [48], [50], [54], [58]- [62], [64], [65], [73], [74], [77]. Moreover, by incorporating these roughness profiles into synthetic aperture radar (SAR) observations [60], [73], [78], [79], investigating their textural properties [77], [80], [81], and analyzing relevant backscattering properties [82]- [85], valuable results can be obtained through composite ROM models that have yet to be reported in existing literature [20], [25], [35], [37], [43], [48], [49], [52]- [59], [61]- [64], [70], [74], [75]- [80], [83]- [93].…”

Section: Introductionmentioning

confidence: 99%

“…To the best of the authors' knowledge, this survey presents an end-to-end approach to SAR remote sensing of the ocean, incorporating a composite roughness modeling of ROM based on directional two-scale WFS formulation under varying sea state conditions that has yet to be reported [9], [29], [30], [37], [44]- [46], [52], [55], [59], [61], [63], [65], [74], [80], [87], [91], [92], [99], [106], [113]- [115], [117]. It encompasses a range of surveys, including numerical modeling of ROM, synthesis of surface texture, analysis of roughness patterns and fractal fluctuations, investigation of SAR texture electromagnetic interactions, and the generation of an optimal reference roughness model.…”

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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Modeling of the internal wave wake of a self-propelled body for radar scattering cross sections

Yang,

You,

Chen

et al. 2024

Ocean Engineering

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Numerical Simulation and Backscattering Characteristics of Freak Waves Based on JONSWAP Spectrum

Cited by 6 publications

References 33 publications

A deep learning-based approach for automatic detection of concrete cracks below the waterline

A deep learning-based approach for automatic detection of concrete cracks below the waterline

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

Modeling of the internal wave wake of a self-propelled body for radar scattering cross sections

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