Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation
arXiv:1907.01149 · doi:10.1109/TGRS.2020.2979908
Abstract
Hyperspectral super-resolution (HSR) is a problem that aims to estimate an image of high spectral and spatial resolutions from a pair of co-registered multispectral (MS) and hyperspectral (HS) images, which have coarser spectral and spatial resolutions, respectively. In this paper 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 non-convex 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.
arXiv comment30 pages, 12 figures, 5 tables. Codes available at https://github.com/REIYANG/GLORIA. To appear in IEEE Transactions on Geoscience and Remote Sensing
References in corpus (13)
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- A convex formulation for hyperspectral image superresolution via subspace-based regularization
- Nonlinear unmixing of hyperspectral images: models and algorithms
- Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
- Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network
- Guaranteed Matrix Completion via Non-convex Factorization
- Hyperspectral Super-Resolution: A Coupled Tensor Factorization Approach
- Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
- Multi-Band Image Fusion Based on Spectral Unmixing
- A Framework for One-Bit and Constant-Envelope Precoding over Multiuser Massive MISO Channels
- Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability
- Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry
- Hybrid Inexact BCD for Coupled Structured Matrix Factorization in Hyperspectral Super-Resolution