Graph-Based Adaptive Network With Spatial-Spectral Features for Hyperspectral Unmixing
Hyperspectral unmixing aims to extract basic material (endmember) spectra and estimate their corresponding fractions (abundances) from observed pixels in hyperspectral images (HSIs). Recently, blind unmixing methods based on autoencoders (AEs) have gained significant attention due to their capabilit...
Saved in:
| Main Authors: | Hua Dong, Xiaohua Zhang, Jinhua Zhang, Hongyun Meng, Licheng Jiao |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11003430/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Dual Embedding Transformer Network for Hyperspectral Unmixing
by: Huadong Yang, et al.
Published: (2025-01-01) -
Autoencoder-Based Hyperspectral Unmixing with Simultaneous Number-of-Endmembers Estimation
by: Atheer Abdullah Alshahrani, et al.
Published: (2025-04-01) -
A multi-domain dual-stream network for hyperspectral unmixing
by: Jiwei Hu, et al.
Published: (2024-12-01) -
Anomaly-Guided Double Autoencoders for Hyperspectral Unmixing
by: Hongyi Liu, et al.
Published: (2025-02-01) -
A Global-to-Local Spectral-Spatial Attention-Based Nonlinearity and Scaled Endmember Variability Parametric Learning Network for Unmixing
by: Yi Zhao, et al.
Published: (2025-01-01)