Paper Info
Title
Generalized Linear Spectral Mixing Model for Spatial-Temporal-Spectral Fusion
Abstract
Image fusion effectively solves the trade-off among spatial resolution, temporal resolution, and spectral resolution of remote sensing sensors. However, most of existing methods focus on the fusion of two of the spatial, temporal, and spectral metrics of remote sensing images. The few spatial-temporal-spectral fusion (STSF) methods available are mainly for fusing Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images, which are not suitable for the characteristics of the spaceborne hyperspectral images (HSIs) with low temporal resolution, such as Hyperion, ZY-1 02D, and PRISMA. For this purpose, we proposed a novel generalized linear spectral mixing model for STSF (GLMM-STSF). In the method, the GLMM is introduced into the STSF problem, and the temporal variations of images at different times are transferred to the endmember and abundance matrix variations of images for estimation. To the best of our knowledge, for the first time, the STSF task of remote sensing images is handled from the perspective of spectral unmixing. Compared with the existing STSF fusion methods, our method targets the task of fusing spaceborne HSI with low temporal and spatial resolutions with multispectral image (MSI) featured by high temporal and spatial resolutions. Taking the STSF of ZY-1 02D hyperspectral and Sentinel-2 multispectral real datasets as an example, comparisons with related state-of-the-art methods demonstrate that our proposed method achieves superior fusion performance.
Year
DOI
Venue
2022
10.1109/TGRS.2022.3188501
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Spatial resolution, Tensors, Task analysis, Sensors, Hyperspectral imaging, Sparse matrices, Earth, Hyperspectral, image fusion, multispectral, spatial resolution, spectral resolution, spectral unmixing, temporal resolution
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Jun Zhou12814.85
Weiwei Sun215.75
Xiangchao Meng302.03
Gang Yang426.10
Kai Ren511.70
Jiangtao Peng615.42