Adaboost-like End-to-End multiple lightweight U-nets for road extraction from optical remote sensing images
Road extraction from optical remote sensing images has many important application scenarios, such as navigation, automatic driving and road network planning, etc. Current deep learning based models have achieved great successes in road extraction. Most deep learning models improve abilities rely on...
Main Authors: | Ziyi Chen, Cheng Wang, Jonathan Li, Wentao Fan, Jixiang Du, Bineng Zhong |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-08-01
|
Series: | International Journal of Applied Earth Observations and Geoinformation |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243421000489 |
Similar Items
-
Enhanced Lightweight End-to-End Semantic Segmentation for High-Resolution Remote Sensing Images
by: He Dong, et al.
Published: (2022-01-01) -
RemainNet: Explore Road Extraction from Remote Sensing Image Using Mask Image Modeling
by: Zhenghong Li, et al.
Published: (2023-08-01) -
RUW-Net: A Dual Codec Network for Road Extraction From Remote Sensing Images
by: Jingyu Yang, et al.
Published: (2024-01-01) -
DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
by: Shuang Zhao, et al.
Published: (2023-07-01) -
Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark
by: Xinyu Zhang, et al.
Published: (2022-09-01)