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

Shahrezaei, Iman Heidarpour; Kim, Hyuncheol

doi:10.1109/jstars.2021.3084822

Cited by 10 publications

(9 citation statements)

References 78 publications

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“…But for drifting ice or water, its coherence is relatively low due to the influences of strong winds and changing ocean currents. Ocean currents will constantly change the distribution of drifting ice and may break up its body with high energy [37]. Therefore, the difference between landfast ice and drifting ice in coherence provides an obvious discrimination (bright and dark) in coherence image.…”

Section: Coherence Imagementioning

confidence: 99%

A Modified Approach of Extracting Landfast Ice Edge Based on Sentinel-1A InSAR Coherence Image in the Gulf of Bothnia

Wang

et al. 2021

JMSE

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Landfast ice is an integral component of the coastal ecosystem. Extracting the edge and mapping the extent of landfast ice are one of the main methods for studying ice changes. In this work, a standardized process for extracting landfast ice edge in the Baltic Sea using the InSAR coherence image is established with Sentinel-1 radar data and InSAR technology. A modified approach combining multiscale segmentation and morphological erosion is then proposed to provide a reliable way to extract landfast ice edge. Firstly, the coherence image is obtained using InSAR technology. Then, the edge is separated and extracted with the modified approach. The modified approach is essentially a four-step procedure involving image segmentation, median filter, morphological erosion, and rejection of small patches. Finally, the full extent of landfast ice can be obtained using floodfill algorithm. Multiple InSAR image pairs of Sentinel-1A acquired from 2018 to 2019 are utilized to successfully extract the landfast ice edge in the Gulf of Bothnia. The results show that the landfast ice edge and the extents obtained by the proposed approach are visually consistent with those shown in the ice chart issued by the Swedish Meteorological and Hydrological Institute (SMHI) over a coastline length of 345 km. The mean distance between land–water boundary and the coastline issued by the National Oceanic and Atmospheric Administration (NOAA) is 109.1 m. The modified approach obviously preserves more details in local edge than the reference method. The experimental results show that the modified approach proposed in this paper can extract the edge and map the extent of landfast ice more accurately and quickly, and is therefore expected to contribute to the further understanding and analyzing the changes of landfast ice in the future.

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Section: Coherence Imagementioning

confidence: 99%

A Modified Approach of Extracting Landfast Ice Edge Based on Sentinel-1A InSAR Coherence Image in the Gulf of Bothnia

Wang

et al. 2021

JMSE

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“…More specifically, although most studies have focused on the physical formulation of WFS [64], [68]- [70], addressing specific problems in distinct domains primarily using either 1D formulas or 2D simulations [71]- [73], there is currently a gap in the literature regarding a comprehensive survey on the 3D modeling of ROM through WFS synthesis and the analysis of roughness patterns in response to sea states [37]. In this context, the WFS formula proves more effective in a two-scale format, referred to as a composite spectrum [74], incorporating major ocean wave modulation effects [37], wideband resonant modulation impacts [37], [63], and associated filtering effects [63], all crucial for determining the spectral distribution and the structural interpretation of the ROM [75], [76]. 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].…”

Section: Introductionmentioning

confidence: 99%

“…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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“…1 With the advent of high-resolution synthetic aperture radar (SAR), the problem of radar electromagnetic wave scattering from random rough surfaces has been widely studied. 2 Shahrezaei et al 3 used a two-scale model to parameterize sea surface roughness by describing the directional wave spectrum based on the SAR backscatter map. Zhou et al 4 retrieved the sea surface wind speed by studying the sea surface wind streak imaging mechanism of SAR to obtain the sea surface roughness and wind direction.…”

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

Cited by 10 publications

References 78 publications

A Modified Approach of Extracting Landfast Ice Edge Based on Sentinel-1A InSAR Coherence Image in the Gulf of Bothnia

A Modified Approach of Extracting Landfast Ice Edge Based on Sentinel-1A InSAR Coherence Image in the Gulf of Bothnia

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

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