Multi-level representation learning via ConvNeXt-based network for unaligned cross-view matching
Cross-view matching refers to the use of images from different platforms (e.g. drone and satellite views) to retrieve the most relevant images, where the key is that the viewpoints and spatial resolution. However, most of the existing methods focus on extracting fine-grained features and ignore the...
Saved in:
Main Authors: | Fangli Guan, Nan Zhao, Zhixiang Fang, Ling Jiang, Jianhui Zhang, Yue Yu, Haosheng Huang |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2025-01-01
|
Series: | Geo-spatial Information Science |
Subjects: | |
Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2439385 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism
by: Shaodan Lin, et al.
Published: (2025-02-01) -
A hybrid multiscale feature fusion model for enhanced cardiovascular arrhythmia detection
by: Md. Alamin Talukder
Published: (2025-03-01) -
Fault diagnosis of mining rolling bearings based on Superlet Transform and OD-ConvNeXt-ELA
by: WU Xinzhong, et al.
Published: (2024-12-01) -
SC-ResNeXt: A Regression Prediction Model for Nitrogen Content in Sugarcane Leaves
by: Zihao Lu, et al.
Published: (2025-01-01) -
An improved lightweight ConvNeXt for rice classification
by: Pengtao Lv, et al.
Published: (2025-01-01)