An Inverted Residual Cross Head Knowledge Distillation Network for Remote Sensing Scene Image Classification
In recent years, remote sensing scene classification (RSSC) has achieved notable advancements. Remote sensing scene images exhibit greater complexity in terms of land features, with large intra class differences and high inter class similarity, posing challenges in effectively extracting discriminat...
Saved in:
Main Authors: | Cuiping Shi, Mengxiang Ding, Liguo Wang |
---|---|
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/10870144/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Frequency and Texture Aware Multi-Domain Feature Fusion for Remote Sensing Scene Classification
by: Russo Ashraf, 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) -
Sample-prototype optimal transport-based universal domain adaptation for remote sensing image classification
by: Xiaosong Chen, et al.
Published: (2024-12-01) -
Enhancing Remote Sensing Semantic Segmentation Accuracy and Efficiency Through Transformer and Knowledge Distillation
by: Kang Zheng, et al.
Published: (2025-01-01) -
TPDTNet: Two-Phase Distillation Training for Visible-to-Infrared Unsupervised Domain Adaptive Object Detection
by: Siyu Wang, et al.
Published: (2025-01-01)