MAMNet: Lightweight Multi-Attention Collaborative Network for Fine-Grained Cropland Extraction from Gaofen-2 Remote Sensing Imagery
To address the issues of high computational complexity and boundary feature loss encountered when extracting farmland information from high-resolution remote sensing images, this study proposes an innovative CNN–Transformer hybrid network, MAMNet. This framework integrates a lightweight encoder, a g...
Saved in:
| Main Authors: | Jiayong Wu, Xue Ding, Jinliang Wang, Jiya Pan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/11/1152 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Lightweight Deep Learning Model, ConvNeXt-U: An Improved U-Net Network for Extracting Cropland in Complex Landscapes from Gaofen-2 Images
by: Shukuan Liu, et al.
Published: (2025-01-01) -
Preliminary analysis of wave retrieval from Chinese Gaofen-3 SAR imagery in the Arctic Ocean
by: Wei-Zeng Shao, et al.
Published: (2022-12-01) -
Effective Cultivated Land Extraction in Complex Terrain Using High-Resolution Imagery and Deep Learning Method
by: Zhenzhen Liu, et al.
Published: (2025-03-01) -
Nearshore topography retrieval based on wave spectrum inverted from Gaofen-3 image
by: Mengyu Hao, et al.
Published: (2025-07-01) -
Enhancing coastal bathymetric mapping with physics-informed recurrent neural networks synergizing Gaofen satellite imagery and ICESat-2 lidar data: A case in the South China Sea
by: Congshuang Xie, et al.
Published: (2025-07-01)