Hyperspectral Image Classification with a Multiscale Fusion-Evolution Graph Convolutional Network Based on a Feature-Spatial Attention Mechanism
Convolutional neural network (CNN) has achieved excellent performance in the classification of hyperspectral images (HSI) due to its ability to extract spectral and spatial feature information. However, the conventional CNN model does not perform well in regions with irregular geometric appearances....
Main Authors: | Haoyu Jing, Yuanyuan Wang, Zhenhong Du, Feng Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-06-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/14/11/2653 |
Similar Items
-
A Hyperspectral Image Classification Method Based on the Nonlocal Attention Mechanism of a Multiscale Convolutional Neural Network
by: Mingtian Li, et al.
Published: (2023-03-01) -
Multiscale cross-fusion network for hyperspectral image classification
by: Haizhu Pan, et al.
Published: (2023-12-01) -
Hyperspectral Image Classification Based on Fusion of Convolutional Neural Network and Graph Network
by: Luyao Gao, et al.
Published: (2023-06-01) -
Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection
by: Jinjin Wang, et al.
Published: (2023-01-01) -
MSLAENet: Multiscale Learning and Attention Enhancement Network for Fusion Classification of Hyperspectral and LiDAR Data
by: Yingying Fan, et al.
Published: (2022-01-01)