Underwater Topography Detection and Analysis of the Qilianyu Islands in the South China Sea Based on GF-3 SAR Images

Huang, Longyu; Yang, Jungang; Meng, Junmin; Zhang, Jie

doi:10.3390/rs13010076

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…According to their causes, the undulation of a rough sea surface can be divided into swell wave and wind wave, which generate different characteristics, such as wave crest lines and wave periods. [19,20] Existing researches of underwater acoustic propagation mainly focus on wind waves, whereas swell waves are rarely considered. On the northern continental slope of the South China Sea, experiments showed that swell waves have a strong influence on the acoustic propagation in the surface sound channel.…”

Section: Introductionmentioning

confidence: 99%

High-order Bragg forward scattering and frequency shift of low-frequency underwater acoustic field by moving rough sea surface

Mo 莫,

Zhang 张,

Lu 鹿

et al. 2024

Chinese Phys. B

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Acoustic scattering modulation caused by an undulating sea surface on the space–time dimension seriously affects underwater detection and target recognition. Herein, underwater acoustic scattering modulation from a moving rough sea surface is studied based on integral equation and parabolic equation. And with the principles of grating and constructive interference, the mechanism of this acoustic scattering modulation is explained. The periodicity of the moving rough sea surface interferes will result in generating interference of the scattering field at a series of discrete angles, respectively forming comb-like and frequency shift characteristics on the intensity and frequency spectrum of the acoustic scattering field, that is a high-order Bragg scattering phenomenon. Unlike the conventional Doppler effect, the frequency shifts of the Bragg scattering phenomenon are multiples of the undulating sea surface frequency and are independent of the incident sound wave frequency. Consequently, even with an incident low-frequency underwater acoustic field, obvious frequency shifts may be generated. Moreover, under the action of ideal sinusoidal waves, swells, fully grown wind waves, unsteady wind waves, or mixed waves, different moving rough sea surfaces afford different acoustic scattering processes and frequency shift characteristics. For the swell wave, which tends to be a single harmonic wave, the moving rough sea surface produced a more obvious high-order scattering and frequency shifts. The same phenomena are observed on the sea surface under fully grown wind waves, however, the frequency shift slightly offsets the multiple peak frequencies of the wind wave spectrum. Compared with the swell and fully grown wind wave, acoustic scattering and frequency shift are not obvious for the sea surface under unsteady wind waves.

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Section: Introductionmentioning

confidence: 99%

High-order Bragg forward scattering and frequency shift of low-frequency underwater acoustic field by moving rough sea surface

Mo 莫,

Zhang 张,

Lu 鹿

et al. 2024

Chinese Phys. B

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“…Since the wavelength range of the electromagnetic signals used by SAR is centimeter to meter, SAR can effectively penetrate concealed objects and clouds [6][7][8][9][10]. In addition, SAR is not affected by illumination conditions and can operate under all-weather, all-day conditions, which is currently an important means of remote sensing observation in many applications [11][12][13][14][15]. Nevertheless, they are more difficult to interpret by human vision than optical images.…”

Section: Introductionmentioning

confidence: 99%

SAR-to-Optical Image Translation via an Interpretable Network

Zhang,

Zhang

et al. 2024

Remote Sensing

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Synthetic aperture radar (SAR) is prevalent in the remote sensing field but is difficult to interpret by human visual perception. Recently, SAR-to-optical (S2O) image conversion methods have provided a prospective solution. However, since there is a substantial domain difference between optical and SAR images, they suffer from low image quality and geometric distortion in the produced optical images. Motivated by the analogy between pixels during the S2O image translation and molecules in a heat field, a thermodynamics-inspired network for SAR-to-optical image translation (S2O-TDN) is proposed in this paper. Specifically, we design a third-order finite difference (TFD) residual structure in light of the TFD equation of thermodynamics, which allows us to efficiently extract inter-domain invariant features and facilitate the learning of nonlinear translation mapping. In addition, we exploit the first law of thermodynamics (FLT) to devise an FLT-guided branch that promotes the state transition of the feature values from an unstable diffusion state to a stable one, aiming to regularize the feature diffusion and preserve image structures during S2O image translation. S2O-TDN follows an explicit design principle derived from thermodynamic theory and enjoys the advantage of explainability. Experiments on the public SEN1-2 dataset show the advantages of the proposed S2O-TDN over the current methods with more delicate textures and higher quantitative results.

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“…With the continuous development of remote sensing technology, optical remote sensing data and synthetic aperture radar (SAR) remote sensing data have been widely leveraged in disaster monitoring, environmental monitoring, resource exploration, and agricultural planning, etc. [1][2][3][4]. Optical remote sensing image data are more representative of what we can observe with the naked eye, which means that these data contain rich spectral information, but capture depends heavily on the clarity of the environment.…”

Section: Introductionmentioning

confidence: 99%

Edge-Preserving Convolutional Generative Adversarial Networks for SAR-to-Optical Image Translation

Guo

Zhang

et al. 2021

Remote Sensing

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With the ability for all-day, all-weather acquisition, synthetic aperture radar (SAR) remote sensing is an important technique in modern Earth observation. However, the interpretation of SAR images is a highly challenging task, even for well-trained experts, due to the imaging principle of SAR images and the high-frequency speckle noise. Some image-to-image translation methods are used to convert SAR images into optical images that are closer to what we perceive through our eyes. There exist two weaknesses in these methods: (1) these methods are not designed for an SAR-to-optical translation task, thereby losing sight of the complexity of SAR images and the speckle noise. (2) The same convolution filters in a standard convolution layer are utilized for the whole feature maps, which ignore the details of SAR images in each window and generate images with unsatisfactory quality. In this paper, we propose an edge-preserving convolutional generative adversarial network (EPCGAN) to enhance the structure and aesthetics of the output image by leveraging the edge information of the SAR image and implementing content-adaptive convolution. The proposed edge-preserving convolution (EPC) decomposes the content of the convolution input into texture components and content components and then generates a content-adaptive kernel to modify standard convolutional filter weights for the content components. Based on the EPC, the EPCGAN is presented for SAR-to-optical image translation. It uses a gradient branch to assist in the recovery of structural image information. Experiments on the SEN1-2 dataset demonstrated that the proposed method can outperform other SAR-to-optical methods by recovering more structures and yielding a superior evaluation index.

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Underwater Topography Detection and Analysis of the Qilianyu Islands in the South China Sea Based on GF-3 SAR Images

Cited by 10 publications

References 21 publications

High-order Bragg forward scattering and frequency shift of low-frequency underwater acoustic field by moving rough sea surface

High-order Bragg forward scattering and frequency shift of low-frequency underwater acoustic field by moving rough sea surface

SAR-to-Optical Image Translation via an Interpretable Network

Edge-Preserving Convolutional Generative Adversarial Networks for SAR-to-Optical Image Translation

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