A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification
Remote sensing scene classification (RSSC) is essential in Earth observation, with applications in land use, environmental status, urban development, and disaster risk assessment. However, redundant background interference, varying feature scales, and high interclass similarity in remote sensing ima...
Saved in:
Main Authors: | Ziwei Li, Weiming Xu, Shiyu Yang, Juan Wang, Hua Su, Zhanchao Huang, Sheng Wu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10742489/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An Inverted Residual Cross Head Knowledge Distillation Network for Remote Sensing Scene Image Classification
by: Cuiping Shi, et al.
Published: (2025-01-01) -
Zero-Shot Remote Sensing Scene Classification Based on Automatic Knowledge Graph and Dual-Branch Semantic Correlation Supervision
by: Chao Wang, et al.
Published: (2025-01-01) -
Frequency and Texture Aware Multi-Domain Feature Fusion for Remote Sensing Scene Classification
by: Russo Ashraf, et al.
Published: (2025-01-01) -
Multiple Hierarchical Cross-Scale Transformer for Remote Sensing Scene Classification
by: Dan Zhang, et al.
Published: (2024-12-01) -
Semantic-enhanced panoptic scene graph generation through hybrid and axial attentions
by: Xinhe Kuang, et al.
Published: (2024-12-01)