Lightweight Deep Learning Model, ConvNeXt-U: An Improved U-Net Network for Extracting Cropland in Complex Landscapes from Gaofen-2 Images
Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep l...
Saved in:
Main Authors: | Shukuan Liu, Shi Cao, Xia Lu, Jiqing Peng, Lina Ping, Xiang Fan, Feiyu Teng, Xiangnan Liu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/25/1/261 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Novel Intelligent Fault Diagnosis Method of Rolling Bearings Based on the ConvNeXt Network with Improved DenseBlock
by: Jiahao Song, et al.
Published: (2024-12-01) -
Fault diagnosis of rotating parts integrating transfer learning and ConvNeXt model
by: Zhikai Xing, et al.
Published: (2025-01-01) -
Radar Waveform Recognition With ConvNeXt and Focal Loss
by: Liping Luo, et al.
Published: (2024-01-01) -
CMPF-UNet: a ConvNeXt multi-scale pyramid fusion U-shaped network for multi-category segmentation of remote sensing images
by: Ning Li, et al.
Published: (2024-01-01) -
Multi-level representation learning via ConvNeXt-based network for unaligned cross-view matching
by: Fangli Guan, et al.
Published: (2025-01-01)