Multiple Hierarchical Cross-Scale Transformer for Remote Sensing Scene Classification
The Transformer model can capture global contextual information but does not have an inherent inductive bias. In contrast, convolutional neural networks (CNNs) are highly praised in computer vision due to their strong inductive bias and local spatial correlation. To combine the advantages of the two...
Saved in:
Main Authors: | Dan Zhang, Wenping Ma, Licheng Jiao, Xu Liu, Yuting Yang, Fang Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-12-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/17/1/42 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Hierarchical Graph-Enhanced Transformer Network for Remote Sensing Scene Classification
by: Ziwei Li, et al.
Published: (2024-01-01) -
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) -
Sample-prototype optimal transport-based universal domain adaptation for remote sensing image classification
by: Xiaosong Chen, et al.
Published: (2024-12-01)