Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach
Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach
复制标题
DOI：
10.1109/tsp.2018.2876362
复制
发表时间：
2018-12-15
影响因子：
5.4
通讯作者：
Ma, Wing-Kin
中科院分区：
文献类型：
--
作者：
Kanatsoulis, Charilaos, I;Fu, Xiao;Ma, Wing-Kin
高光谱超分辨率是指融合高光谱图像（HSI）和多光谱图像（MSI）以生成具有良好空间和光谱分辨率的超分辨率图像（SRI）的问题。最先进的方法通过对矩阵化的HSI和MSI进行低秩矩阵近似来解决该问题。这些方法在一定程度上是有效的，但仍存在一些挑战。首先，HSI和MSI本质上是三阶张量（数据“立方体”），因此矩阵化容易丢失结构信息，这可能会降低性能。其次，不清楚这些基于低秩矩阵的融合策略在实际假设下是否能保证SRI的可识别性。然而，可识别性在估计问题中起着关键作用，通常对实际性能有重大影响。第三，大多数现有方法假定从SRI到相应的HSI和MSI的退化算子是已知的（或容易估计的），但在实际中情况并非如此。在本文中，我们建议从张量的角度解决超分辨率问题。具体来说，我们利用HSI和MSI的多维结构提出了一个耦合张量分解框架，该框架可以有效克服上述问题。所提出的方法在温和且实际的条件下保证了SRI的可识别性。此外，它在对退化算子了解甚少的情况下也能工作，这在实践中显然是一个有利的特征。通过模拟半真实场景展示了所提方法的有效性。
Hyperspectral super-resolution refers to the problem of fusing a hyperspectral image (HSI) and a multispectral image (MSI) to produce a super-resolution image (SRI) that admits fine spatial and spectral resolutions. State-of-the-art methods approach the problem via low-rank matrix approximations to the matricized HSI and MSI. These methods are effective to some extent, but a number of challenges remain. First, HSIs and MSIs are naturally third-order tensors (data "cubes") and thus matricization is prone to a loss of structural information, which could degrade performance. Second, it is unclear whether these low-rank matrix-based fusion strategies can guarantee the identifiability of the SRI under realistic assumptions. However, identifiability plays a pivotal role in estimation problems and usually has a significant impact on practical performance. Third, a majority of the existing methods assume known (or easily estimated) degradation operators from the SRI to the corresponding HSI and MSI, which is hardly the case in practice. In this paper, we propose to tackle the super-resolution problem from a tensor perspective. Specifically, we utilize the multidimensional structure of the HSI and MSI to propose a coupled tensor factorization framework that can effectively overcome the aforementioned issues. The proposed approach guarantees the identifiability of the SRI under mild and realistic conditions. Furthermore, it works with little knowledge about the degradation operators, which is clearly a favorable feature in practice. Semi-real scenarios are simulated to showcase the effectiveness of the proposed approach.