An Enhanced Global Feature-Guided Network Based on Multiple Filtering Noise Reduction for Remote Sensing Image Compression

Shi, Cuiping; Shi, Kaijie; Zhu, Fei; Zeng, Zexin; Ding, Mengxiang; Jin, Zhan

doi:10.1109/tgrs.2024.3483871

IEEE Trans. Geosci. Remote Sensing

2024

DOI: 10.1109/tgrs.2024.3483871

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An Enhanced Global Feature-Guided Network Based on Multiple Filtering Noise Reduction for Remote Sensing Image Compression

Cuiping Shi,

Kaijie Shi,

Fei Zhu

et al.

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2024

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Cited by 3 publications

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An hybrid fuzzy based deep convolutional neural networks for big data based hyper-spectral image classification in agricultural applications

Barath,

Ramesh

2024

Australian Journal of Electrical and Electronics Engineering

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An hybrid fuzzy based deep convolutional neural networks for big data based hyper-spectral image classification in agricultural applications

Barath,

Ramesh

2024

Australian Journal of Electrical and Electronics Engineering

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Anisotropic Bayesian linearized stochastic seismic inversion with multi-parameter decoupling

Yu,

Shi,

Tian

et al. 2024

Journal of Geophysics and Engineering

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Shale oil reservoir emerges as a significant unconventional energy source, commonly predicted by anisotropic seismic inversion. Considering the intricate nature of shale oil reservoirs, it becomes imperative to consider uncertainties during anisotropic inversion. An effective approach to address this involves stochastic inversion, specifically the anisotropic Bayesian linearized inversion (ABLI), which characterizes statistical and spatial correlations of subsurface parameters through a crucial multivariate correlation matrix constructed through geostatistics. However, an inevitable challenge in stochastic inversion arises from interference during the calibration of statistical and spatial correlations of subsurface parameters. This challenge becomes particularly pronounced in anisotropic inversion, heightened by the multitude of involved model parameters. Existing decorrelation approaches primarily address statistical correlation, neglecting the impact of spatial correlation. To tackle this issue, a novel multi-parameter decoupling strategy is proposed, formulating decoupling anisotropic Bayesian linearized inversion (D-ABLI). D-ABLI introduces an advanced decorrelation approach, and uses principal component analysis (PCA) to simultaneously eliminate impact of statistical and spatial correlations on ABLI. The decoupling enhances the inversion accuracy of model parameters in ABLI, particularly for density and anisotropic parameters. The theoretical underpinnings of the decoupling strategy are demonstrated to be reasonable, and the effectiveness of D-ABLI is proved through a theoretical data test and a field data test regarding shale oil reservoirs. The D-ABLI results offer the capability to estimate fracture density accurately and unveil the distribution of shale oil.

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3D high-resolution radon transform based on a strong sparseLp–1norm and its applications

Shi,

Wang,

Shi

et al. 2024

Journal of Geophysics and Engineering

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Multiple reflections are among the most challenging noises to suppress in seismic data, as they differ from effective waves only in terms of apparent velocity. Besides, the Radon transform, an essential technique for attenuating multiple reflections, has been widely incorporated into various commercial software packages. Thus, this study introduces a 3D Radon transform method based on the LP‒1 norm to enhance sparsity-constraining capability in the transform domain, leveraging high-resolution Radon transform techniques. Specifically, an iteratively reweighted least squares (IRLS) algorithm is employed to obtain the transformed data in the Radon domain. Given that the LP‒1 norm is applied to seismic data processing for the first time, this paper theoretically demonstrates its powerful sparsity-constraining capability. Indeed, the proposed strategy enhances energy concentration in the Radon transform domain, better-separating primaries from multiples and ultimately suppressing the multiples. Both model tests and real data indicate that the 3D Radon transform constrained by the LP‒1 norm outperforms existing high-resolution Radon transform methods with sparsity constraints regarding energy concentration and effectiveness in multiple reflection attenuation.

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An Enhanced Global Feature-Guided Network Based on Multiple Filtering Noise Reduction for Remote Sensing Image Compression

Cited by 3 publications

References 56 publications

An hybrid fuzzy based deep convolutional neural networks for big data based hyper-spectral image classification in agricultural applications

An hybrid fuzzy based deep convolutional neural networks for big data based hyper-spectral image classification in agricultural applications

Anisotropic Bayesian linearized stochastic seismic inversion with multi-parameter decoupling

3D high-resolution radon transform based on a strong sparseLp–1norm and its applications

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