Modeling and SAR imaging of the sea surface: A review of the state-of-the-art with simulations

Rizaev, Igor G.; Karakuş, Oktay; Hogan, S. J.; Achim, Alin

doi:10.1016/j.isprsjprs.2022.02.017

Cited by 27 publications

(16 citation statements)

References 140 publications

(309 reference statements)

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“…In the fourth and the fifth experiment, two simulated images of the sea surface were used as the original scene, with the first one only including sea waves, and the second including a travelling ship and its wake as well. Simulated as the scenes are, they are not as simple as Scene 1, which is a mere combination of black and white regions resembling point reflectors, but are rather based on an exquisite model taking the most important SAR imaging effects into account [29]. The scenes are based on a model of the sea surface using the Pierson-Moskowitz spectrum and cosine-squared spreading function with wind speed V w = 8 m/s for the first image and V w = 4 m/s for the second image, with waves traveling at 45 • relative to the SAR flight direction.…”

Section: Resultsmentioning

confidence: 99%

“…When validating the performance of our methods, we mainly focus on datasets representing the sea surface, i.e., real SAR images containing ships and simulated images of sea surface containing sea wave and ship wakes. The autofocusing of this kind of SAR image can be beneficial for many practical applications, including ship detection [28], the characterization of ship wakes [29], and so on. The source code used to produce the presented results has been made available on GitHub at https://github.com/zy-zhangc/SARautofocusing-with-a-non-convex-penalty (accessed on 3 March 2022).…”

Section: Introductionmentioning

confidence: 99%

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Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

et al. 2022

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In this paper, SAR image reconstruction with joint phase error estimation (autofocusing) is formulated as an inverse problem. An optimization model utilising a sparsity-enforcing Cauchy regularizer is proposed, and an alternating minimization framework is used to solve it, in which the desired image and the phase errors are estimated alternatively. For the image reconstruction sub-problem (f-sub-problem), two methods are presented that are capable of handling the problem’s complex nature. Firstly, we design a complex version of the forward-backward splitting algorithm to solve the f-sub-problem iteratively, leading to a complex forward-backward autofocusing method (CFBA). For the second variant, techniques of Wirtinger calculus are utilized to minimize the cost function involving complex variables in the f-sub-problem in a direct fashion, leading to Wirtinger alternating minimization autofocusing (WAMA) method. For both methods, the phase error estimation sub-problem is solved by simply expanding and observing its cost function. Moreover, the convergence of both algorithms is discussed in detail. Experiments are conducted on both simulated and real SAR images. In addition to the synthetic scene employed, the other SAR images focus on the sea surface, with two being real images with ship targets, and another two being simulations of the sea surface (one of them containing ship wakes). The proposed method is demonstrated to give impressive autofocusing results on these datasets compared to state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

et al. 2022

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“…In the literature [ 23 ], a sea surface target motion parameter estimation algorithm was proposed to perform a two-level low-rank plus sparse decomposition of the wake in SAR images using Radon transform, which effectively improves the detection accuracy of wake. In the literature [ 24 ], a ship wake detection method for complex marine environments is proposed, where waves and ship wake are superimposed to simulate real sea surface SAR images to improve the adaptability of the wake detection algorithm. In the literature [ 25 ], a CNN-based optical image wake detection method is proposed and a novel wake detector (WAKENET) is designed to improve the accuracy of wake detection.…”

Section: Related Workmentioning

confidence: 99%

An Improved Wake Vortex‐Based Inversion Method for Submarine Maneuvering State

Kong

Yang

Cai

et al. 2023

Computational Intelligence and Neuroscience

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As the noise reduction performance of submarines continues to improve, it is difficult to detect and track submarines through acoustic detection techniques. Therefore, nonacoustic submarine detection techniques are becoming more and more important. The submarine movement will leave a wake vortex, and the information of the wake vortex can be used to invert the maneuvering state of the submarine. However, the wake vortex is constantly dissipated in the evolution process, and the strength of the wake vortex is constantly reduced, resulting in the gradual weakening of the characteristics of the wake vortex, which makes the inversion of submarine operating state difficult and less accurate. In order to solve the above problems, this paper proposes an improved wake vortex-based inversion method for submarine maneuvering state. Firstly, a random finite set of submarine wake vortex observation features is established to obtain the feature with the highest correlation degree with submarine maneuvering state in the random finite set. Secondly, the multiscale fusion module and attention mechanism are used to re-encode the weak features of the wake vortex image, and the salient features of the wake vortex image are extracted. Finally, the manipulation state of the wake vortex image is retrieved by the extracted salient features. The experimental results show that the average inversion accuracy of the proposed algorithm is improved by 1.27% in terms of manipulating state inversion of weak feature wake vortex images. The algorithm in this paper can realize the inversion of submarine maneuvering state in the case of weak submarine wake vortex image features and incomplete feature information. It provides the basis for the detection technology based on the submarine wake characteristics.

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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%

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 and SAR imaging of the sea surface: A review of the state-of-the-art with simulations

Cited by 27 publications

References 140 publications

Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

Sparse Regularization with a Non-Convex Penalty for SAR Imaging and Autofocusing

An Improved Wake Vortex‐Based Inversion Method for Submarine Maneuvering State

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

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