Hyperspectral Super-Resolution via Global–Local Low-Rank Matrix Estimation
Hyperspectral Super-Resolution via Global–Local Low-Rank Matrix Estimation
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DOI：
10.1109/tgrs.2020.2979908
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发表时间：
2019-07
影响因子：
8.2
通讯作者：
Ruiyuan Wu;Wing-Kin Ma;Xiao Fu;Qiang Li
中科院分区：
文献类型：
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作者：
Ruiyuan Wu;Wing-Kin Ma;Xiao Fu;Qiang Li
高光谱超分辨率(HSR)是从多光谱(MS)和高光谱(HS)图像联合配准的高光谱和高空间分辨率图像中估计高光谱和高空间分辨率的问题。在这篇文章中，我们追求一种高铁比的低阶矩阵估计方法。我们假设与整个图像和图像局部区域相关的光谱-空间矩阵具有低阶结构。尤其是，局部低等级假设的目的是提供一种更灵活的模型来解释端元可变性引起的局部变异效应。我们将HSR问题描述为一个全局-局部秩正则最小二乘问题。通过利用非凸大规模优化的最新进展，即光滑Schatten-$p$逼近和加速优化-最小化方法，我们提出了一种求解全局-局部低阶问题的有效算法。在合成数据、半真实数据和真实数据上的数值实验表明，该算法在恢复性能方面优于一些基准算法。
Hyperspectral super-resolution (HSR) is a problem that aims to estimate an image of high spectral and spatial resolutions from a pair of coregistered multispectral (MS) and hyperspectral (HS) images, which have coarser spectral and spatial resolutions, respectively. In this article, we pursue a low-rank matrix estimation approach for HSR. We assume that the spectral–spatial matrices associated with the whole image and the local areas of the image have low-rank structures. The local low-rank assumption, in particular, has the aim of providing a more flexible model for accounting for local variation effects due to endmember variability. We formulate the HSR problem as a global–local rank-regularized least-squares problem. By leveraging on the recent advances in nonconvex large-scale optimization, namely the smooth Schatten- $p$ approximation and the accelerated majorization–minimization method, we develop an efficient algorithm for the global–local low-rank problem. Numerical experiments on synthetic, semi-real, and real data show that the proposed algorithm outperforms a number of benchmark algorithms in terms of recovery performance.