Abstract
Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.
Highlights
Hyperspectral imagery utilizes a broad range of the electromagnetic spectrum to obtain information of the imaged scene, allowing better identification of materials or detection of processes
The most important deficiency of the Bayesian framework is that regularization terms are required that should comprehensively represent spatial information of hyperspectral image (HSI), which is partially lost by matricizing the HSI
We extend negative matrix factorization (NMF) to a tensor framework, applying a nonconstraints, in this paper, the spatio-spectral joint structures of HSIs are preserved without negative tensor decomposition
Summary
Hyperspectral imagery utilizes a broad range of the electromagnetic spectrum to obtain information of the imaged scene, allowing better identification of materials or detection of processes. As each band of a HSI contains the spectral response to a narrow interval of the electromagnetic spectrum, it is necessary to collect reflectance from a wider area on the scene, decreasing the spatial resolution of HSIs. HSI acquisition instruments have extensive limitations for capturing high-resolution images, and there is always a tradeoff between spectral resolution and spatial resolution. We always need spatial information provided from outside; using an auxiliary image consisting of panchromatic image, multispectral image or an RGB one is a well-known approach. All fusion approaches can be grouped in the following categories: methods using a Bayesian framework [6,11,14,15,16,17,18], matrix factorization based methods [4,5,12,19,20,21], tensor factorization based methods [1,3,9,22,23,24,25] and deep learning based methods [26,27,28]
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