EANet: Edge-Aware Network for the Extraction of Buildings from Aerial Images
Deep learning methods have been used to extract buildings from remote sensing images and have achieved state-of-the-art performance. Most previous work has emphasized the multi-scale fusion of features or the enhancement of more receptive fields to achieve global features rather than focusing on low...
Main Authors: | Guang Yang, Qian Zhang, Guixu Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-07-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/12/13/2161 |
Similar Items
-
Precise Extraction of Buildings from High-Resolution Remote-Sensing Images Based on Semantic Edges and Segmentation
by: Liegang Xia, et al.
Published: (2021-08-01) -
Building Outline Extraction Directly Using the U<sup>2</sup>-Net Semantic Segmentation Model from High-Resolution Aerial Images and a Comparison Study
by: Xinchun Wei, et al.
Published: (2021-08-01) -
Edge Detection With Direction Guided Postprocessing for Farmland Parcel Extraction
by: Yusen Xie, et al.
Published: (2023-01-01) -
MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery
by: Yue Qiu, et al.
Published: (2022-08-01) -
MLCRNet: Multi-Level Context Refinement for Semantic Segmentation in Aerial Images
by: Zhifeng Huang, et al.
Published: (2022-03-01)