Pan-sharpening via conditional invertible neural network

Wang, Jiaming; Lu, Tao; Huang, Xiao; Zhang, Ruiqian; Feng, Xiaoxiao

doi:10.1016/j.inffus.2023.101980

Cited by 10 publications

(1 citation statement)

References 35 publications

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“…Bayesian decision theory estimates unknown states with subjective probabilities under incomplete information, then modifies their occurrence probabilities with the Bayesian formula, and finally makes optimal decisions by using the modified probability 10 .…”

Section: Bayesian Decision Theorymentioning

confidence: 99%

Bayesian decision based fusion algorithm for remote sensing images

Wu,

Jiang,

Zhu

et al. 2024

Sci Rep

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Remote sensing image fusion is dedicated to obtain a high-resolution multispectral (HRMS) image without spatial or spectral distortion compared to the single source image. In this paper, a novel fusion algorithm based on Bayesian estimation for remote sensing images is proposed from the new perspective of risk decisions. In this study, an observation model based on Bayesian estimation for remote sensing image fusion is constructed. Three categories of probabilities including prior, conditional and posterior probabilities are calculated after an intensity-hue-saturation (IHS) transformation is applied to the original low-resolution MS image. To obtain the desired HRMS image, with the corrected posterior probability, a fusion rule based on Bayesian decisions is designed to estimate which pixels to select from the panchromatic (PAN) image and the intensity component of the MS image. The selected pixels constitute a new component that will participate in an IHS inverse transformation to yield the fused image. Extensive experiments were performed on the Pleiades, WorldView-3, and IKONOS datasets, and the results demonstrate the effectiveness of the proposed method.

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Section: Bayesian Decision Theorymentioning

confidence: 99%