Dual Embedding Transformer Network for Hyperspectral Unmixing
Hyperspectral unmixing is an essential task for achieving accurate perception of hyperspectral remote sensing information, aiming to overcome the limitation of spatial resolution and interpret the distribution of land features. To achieve the spatial and spectral feature representation of hyperspect...
Saved in:
Main Authors: | Huadong Yang, Chengbi Zhang |
---|---|
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/10818529/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hyperspectral Image Classification Using Spectral-Spatial Dual Random Fields With Gaussian and Markov Processes
by: Yaqiu Zhang, et al.
Published: (2025-01-01) -
Evaluating Normalization Methods for Robust Spectral Performance Assessments of Hyperspectral Imaging Cameras
by: Siavash Mazdeyasna, et al.
Published: (2025-01-01) -
HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing
by: Qianru Liu, et al.
Published: (2025-01-01) -
Hyperspectral Image Classification Based on Attentional Residual Networks
by: Ning Wang, et al.
Published: (2025-01-01) -
Latent spectral-spatial diffusion model for single hyperspectral super-resolution
by: Yingsong Cheng, et al.
Published: (2024-12-01)