Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition

Autor:	Paul Scheunders, Kamran Kazemi, Marzieh Zare, Mohammad Sadegh Helfroush
Rok vydání:	2021
Předmět:	Multilinear map Economics Computer science Science Multispectral image 0211 other engineering and technologies 02 engineering and technology image fusion non-negative Tucker tensor decomposition Matrix decomposition Non-negative matrix factorization Tensor (intrinsic definition) 0202 electrical engineering electronic engineering information engineering high resolution multispectral image low resolution hyperspectral image multiplicative update rules Biology Image resolution 021101 geological & geomatics engineering Image fusion Physics Hyperspectral imaging Chemistry Computer Science::Computer Vision and Pattern Recognition General Earth and Planetary Sciences 020201 artificial intelligence & image processing Engineering sciences. Technology Algorithm
Zdroj:	Remote sensing Remote Sensing; Volume 13; Issue 15; Pages: 2930 Remote Sensing, Vol 13, Iss 2930, p 2930 (2021)
ISSN:	2072-4292
DOI:	10.3390/rs13152930
Popis:	Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI) to produce a fused high spatio-spectal resolution one, referred to as HSI super-resolution, has recently attracted increasing research interests. In this paper, a new method based on coupled non-negative tensor decomposition (CNTD) is proposed. The proposed method uses tucker tensor factorization for low resolution hyperspectral image (LR-HSI) and high resolution multispectral image (HR-MSI) under the constraint of non-negative tensor ecomposition (NTD). The conventional non-negative matrix factorization (NMF) method essentially loses spatio-spectral joint structure information when stacking a 3D data into a matrix form. On the contrary, in NMF-based methods, the spectral, spatial, or their joint structures must be imposed from outside as a constraint to well pose the NMF problem, The proposed CNTD method blindly brings the advantage of preserving the spatio-spectral joint structure of HSIs. In this paper, the NTD is imposed on the coupled tensor of HIS and MSI straightly. Hence the intrinsic spatio-spectral joint structure of HSI can be losslessly expressed and interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled by means of the core tensor of the Tucker tensor decomposition. The proposed method is completely straight forward and easy to implement. Unlike the other state-of-the-art methods, the complexity of the proposed CNTD method is quite linear with the size of the HSI cube. Compared with the state-of-the-art methods experiments on two well-known datasets, give promising results with lower complexity order.
Databáze:	OpenAIRE
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