Three-Branch Multilevel Attentive Fusion Network for Hyperspectral Pansharpening
- Resource Type
- Conference
- Authors
- Guan, Peiyan; Lam, Edmund Y.
- Source
- IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2022 - 2022 IEEE International. :1087-1090 Jul, 2022
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Signal Processing and Analysis
Image resolution
Correlation
Fuses
Merging
Geoscience and remote sensing
Pansharpening
Feature extraction
Hyperspectral pansharpening
three-branch
multilevel fusion
attention mechanism
- Language
- ISSN
- 2153-7003
In this paper, we propose a three-branch multilevel attentive fusion network (TMA-Net) for hyperspectral pansharpening, which aims to merge low-resolution hyperspectral images (LR-HSIs) and high-resolution panchromatic images (HR-PANs) to obtain HSIs with high resolution. We construct three branches to extract rich features of the two images and the correlation between them, which enables us to capture abundant useful information for pansharpening. We merge the multilevel features extracted by each branch in multiple steps to fully fuse the useful information. An attentive fusion module (AFM) is designed to guide the fusion procedure. It explores the relation between different features and employs attention mechanism to refine them adaptively. The experimental results illustrate the superiority of the TMA-Net.