Fusion of Hyperspectral image and multispectral image based on two-branch spectral unmixing network

Image fusion is a mainstream approach to improve the spatial resolution of hyperspectral images (HSI). This paper proposes a fusion method of HSI and multispectral image(MSI) based on a novel two-branch spectral unmixing network which is divided into a fusion branch and a residual branch. Fusion branch firstly extracts endmembers of the input HSI, then fuses the HSI and MSI with a PAN image to obtain high resolution abundances. So the high resolution HSI is acquired by combining endmembers and abundances. In addition, considering the effect of multiple non-linear factors, a residual branch is introduced in this work to complement spectral and spatial details. Finally, the results of fusion branch and residual branches are combined to obtain the more accurate reconstruction HSI. Experiments on both simulated and real datasets show the effectiveness of this work.

Authors

Institutions

Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1145/3703935.3704098
Primary Topic
Advanced Image Fusion Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fusion of Hyperspectral image and multispectral image based on two-branch spectral unmixing network

Chengyong Wang, Yuquan Gan, Chen Yi, Zhuoxin Liu
Advanced Image Fusion Techniques
article

Fusion of Hyperspectral image and multispectral image based on two-branch spectral unmixing network

Chengyong Wang, Yuquan Gan, Chen Yi, Zhuoxin Liu
article en

Abstract

Image fusion is a mainstream approach to improve the spatial resolution of hyperspectral images (HSI). This paper proposes a fusion method of HSI and multispectral image(MSI) based on a novel two-branch spectral unmixing network which is divided into a fusion branch and a residual branch. Fusion branch firstly extracts endmembers of the input HSI, then fuses the HSI and MSI with a PAN image to obtain high resolution abundances. So the high resolution HSI is acquired by combining endmembers and abundances. In addition, considering the effect of multiple non-linear factors, a residual branch is introduced in this work to complement spectral and spatial details. Finally, the results of fusion branch and residual branches are combined to obtain the more accurate reconstruction HSI. Experiments on both simulated and real datasets show the effectiveness of this work.

Xi’an University of Posts and Telecommunications (CN)
National Natural Science Foundation of China, Natural Science Basic Research Program of Shaanxi Province
Openalex Percentile: Top 56%
Advanced Image Fusion Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.