A Nonconvex Pansharpening Model With Spatial and Spectral Gradient Difference-Induced Nonconvex Sparsity Priors
A Nonconvex Pansharpening Model With Spatial and Spectral Gradient Difference-Induced Nonconvex Sparsity Priors
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具有空间和谱梯度差诱导非凸稀疏先验的非凸全色锐化模型
DOI：
10.1109/tgrs.2021.3078334
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发表时间：
2022
影响因子：
8.2
通讯作者：
Liang Xiao
中科院分区：
文献类型：
--
作者：
Pengfei Liu;Liang Xiao
本文提出了一个非凸变量模型，用于用空间和光谱梯度差异诱导的非凸率较高率（PSSGDNSP），该模型可以融合Panchrostic（PAN）和低分辨率（LR）多光谱（MS）图像与GE与GE融合在一起
This article proposed a nonconvex variational model for pansharpening with spatial and spectral gradient difference-induced nonconvex sparsity priors (PSSGDNSP), which can fuse the panchromatic (Pan) and low-resolution (LR) multispectral (MS) images to generate the high-resolution (HR) MS image. More particularly, the proposed PSSGDNSP model exploits the spatial gradient difference-induced nonconvex <inline-formula> <tex-math notation="LaTeX">$l_{1/2}$ </tex-math></inline-formula> sparsity prior between HR MS and Pan, and the spectral gradient difference-induced nonconvex <inline-formula> <tex-math notation="LaTeX">$l_{1/2}$ </tex-math></inline-formula> sparsity prior between HR and LR MS. Consequently, our proposed PSSGDNSP model well preserves both the spatial and spectral information. In fact, our proposed band-coupled model treats the MS image like a third-order tensor so that the intrinsic band correlation of the MS image can be fully kept. Moreover, we solve our proposed PSSGDNSP model by applying the alternating direction method of multipliers (ADMM) method. Finally, the experiments fully validate the superiority and performance of our proposed PSSGDNSP method.
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影响因子：
18.6
作者：
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影响因子：
1.3
作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/rsip.2017.7958794
发表时间：
2017-05
期刊：
2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)
影响因子：
--
作者：
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通讯作者：
Yancong Wei;Qiangqiang Yuan
影响因子：
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作者：
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通讯作者：
Chartrand, Rick