A smart meshing technique to speed up radar cross-section calculation of very large random rough surfaces

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

“…It is worth noting that, despite anecdotal evidence either on the sole use of numerical methods or on data interpretations of ocean surface [38], [40], [47], [56], [58], [62]- [65], a survey article addressing the numerical modeling of ROM roughness and its structural interpretation [66], [67], with a focus on the stochastic distribution of WFS under varying sea state conditions, has yet to be a published. 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]. 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],…”

“…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].…”

“…The survey extends beyond ROM modeling, encompassing the synthesis of ocean surface roughness and its electromagnetic interactions, with a specific focus on the intricate dynamics of texture structures as manifested through scattering patterns, scattering orientations, and variations in wave height details, known as roughness fluctuations [43], [48], [50], [63], [66], [77], [82], [91], [92], [113], [118], [121], [123]. Given the dependency of directional WFS formulation on both spreading function and its boundary conditions, which includes the effects of sea states [84], [108], [112], no ROM model generated thus far outperforms others in terms of representing reference roughness pattern and texture characteristics [45], [49], [56], [63], [65], [77], [88], [89], [93], [99], [103], [108], [113], [117], [120]. To address this limitation, a multi-scale transform domain (MTD) fusion method is proposed [124]- [127], incorporating a convolutional neural network (CNN) [128], [129], enabling the reconstruction of a fused roughness model independent of spreading functions as a reference ROM [50], [80], [103], [129], and facilitating the generation of an optimized SAR raw data …”

“…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.…”