Paper Info
Title
A Locally Optimized Model for Hyperspectral and Multispectral Images Fusion
Abstract
The maintenance of spectral variability between subclass objects and the relationship between hyperspectral (HS) bands have been a fundamental but challenging problem for fusing low spatial resolution (LR) HS and high spatial resolution (HR) multispectral (MS) images. This article presents a locally optimized image segmentation fusion (LOISF) framework for HS super-resolution reconstruction. First, LR HS and HR MS are clustered and segmented, and the label attributes of the segmented objects are identified by the prior information. Then, a novel joint fusion model for different typical ground objects is constructed based on spectral unmixing. The fusion problem is formulated mathematically as a convex optimization of a Frobenius norm, which includes spatial, spectral, and index constraints, with an alternating-directions & x2019; optimization featuring linearization providing the solution. Experimental results demonstrate that the proposed LOISF preserves both spatial details and texture, achieving high spectral fidelity, and yielding significantly improved image quality compared to other state-of-the-art fusion methods.
Year
DOI
Venue
2022
10.1109/TGRS.2021.3133670
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Spatial resolution, Matrix decomposition, Image segmentation, Indexes, Sparse matrices, Learning systems, Image fusion, Hyperspectral (HS), image fusion, locally optimized image segmentation fusion (LOISF), multispectral, unmixing
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Kai Ren111.70
Weiwei Sun215.75
Xiangchao Meng3105.20
Gang Yang426.10
Jiangtao Peng515.42
Jingfeng Huang610923.94