Pansharpening Based on Variational Fractional-Order Geometry Model and Optimized Injection Gains

Yang, Yong; Lu, Hangyuan; Huang, Shuying; Wan, Weiguo; Li, Luyi

doi:10.1109/jstars.2022.3154642

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…For parameter setting, this paper sets the initial value of the injection volume coefficient g 0 = 0.1 and the step size r = 0.05, to thoroughly consider the efficiency and precision of the calculation. The Gaussian filter window is set to 5 × 5, the default setting in reference [ 22 ].…”

Section: Analysis Of Experiments Resultsmentioning

confidence: 99%

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An Adaptive Injection Model for Pansharpening

Song

Chen

Ren

et al. 2023

Computational Intelligence and Neuroscience

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Pansharpening technology is used to acquire a multispectral image with high spatial resolution from a panchromatic (PAN) image and a multispectral (MS) image. The detail injection model is popular for its flexibility. However, the accuracy of the injection gain and the extracted details may greatly influence the quality of the pansharpened image. This paper proposes an adaptive injection model to solve these problems. For detail extraction, we present a Gaussian filter estimation algorithm by exploring the intrinsic character of the MS sensor and convolving the PAN image with the filter to adaptively optimize the details to be consistent with the character of the MS image. For the adaptive injection coefficient, we iteratively adjust the coefficient by balancing the spectral and spatial fidelity. By multiplying the optimized details and injection gain, the final HRMS is obtained with the injection model. The performance of the proposed model is analyzed and a large number of tests are carried out on various satellite datasets. Compared to some advanced pansharpening methods, the results prove that our method can achieve the best fusion quality both subjectively and objectively.

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Section: Analysis Of Experiments Resultsmentioning

confidence: 99%

“…However, they require much more computational resources [ 21 ]. Besides, the majority of the previously discussed VO methods rely on the regularization parameters, which need to be determined manually and may affect the accuracy of the model [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Injection Model for Pansharpening

Song

Chen

Ren

et al. 2023

Computational Intelligence and Neuroscience

Self Cite

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“…Recent methods often preserve the consistency of the highpass filtered components of HRMS image and PAN image in order to improve the extraction accuracy of image details. For example, the nonconvex p sparse prior [15], [16], the fractional-order geometry prior [28], and the sparse prior based on cartoon-texture similarities [29]. The aforementioned global sparse modeling is not accurate enough to express the relationship between the latent HRMS image and PAN image.…”

Section: Enhanced Spatial Fidelitymentioning

confidence: 99%

Pansharpening by Combining Enhanced Spatial-Spectral Fidelity and Gradient-Domain Guided Filtering

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Pansharpening techniques fuse the complementary information from a high-resolution panchromatic (PAN) and low-resolution multispectral (LRMS) images to produce a highresolution multispectral (HRMS) image. However, most of the existing pansharpening methods have been affected in the fullresolution domain due to both the absence of ground truths and unavoidable unknown noises. To address this problem, a new pansharpening method has been proposed that combines enhanced sparse models and gradient-domain guided image filtering. Specifically, a deep multiscale Laplacian pyramid super-resolution network improves the resolution of the original LRMS image instead of bicubic interpolation. Then, the accurate preservation of spatialspectral characteristics is achieved in a variational framework with enhanced spatial-spectral fidelity in the image gradient domain. Meanwhile, the gradient-domain guided image filter is used to effectively improve the extraction accuracy of spatial characteristics from the PAN image. Finally, the enhanced sparse regularization on the latent HRMS image is designed to remove noise and artifacts while promoting piecewise-smooth solutions. The experimental results on public satellite datasets demonstrate the superiority of the proposed method against existing pansharpening methods in terms of both full-resolution performance indexes and visual quality.

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Adaptive hybrid pansharpening: a novel approach for combining two methods to achieve superior pansharpening performance

Yılmaz

2023

International Journal of Remote Sensing

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Pansharpening Based on Variational Fractional-Order Geometry Model and Optimized Injection Gains

Cited by 7 publications

References 48 publications

An Adaptive Injection Model for Pansharpening

An Adaptive Injection Model for Pansharpening

Pansharpening by Combining Enhanced Spatial-Spectral Fidelity and Gradient-Domain Guided Filtering

Adaptive hybrid pansharpening: a novel approach for combining two methods to achieve superior pansharpening performance

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