Boundary-Aware Refined Network for Automatic Building Extraction in Very High-Resolution Urban Aerial Images
Convolutional Neural Networks (CNNs), such as U-Net, have shown competitive performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial images. However, due to the unstable multi-scale context aggregation, the insufficient combination of multi-level features and the l...
Main Authors: | Yuwei Jin, Wenbo Xu, Ce Zhang, Xin Luo, Haitao Jia |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-02-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/4/692 |
Similar Items
-
Earthquake-Damaged Buildings Detection in Very High-Resolution Remote Sensing Images Based on Object Context and Boundary Enhanced Loss
by: Chao Wang, et al.
Published: (2021-08-01) -
Auto-identification of linear archaeological traces of the Great Wall in northwest China using improved DeepLabv3+ from very high-resolution aerial imagery
by: Shu Yang, et al.
Published: (2022-09-01) -
Characterizing Small-Town Development Using Very High Resolution Imagery within Remote Rural Settings of Mozambique
by: Dong Chen, et al.
Published: (2021-08-01) -
CCENet: Cascade Class-Aware Enhanced Network for High-Resolution Aerial Imagery Semantic Segmentation
by: Qixiong Wang, et al.
Published: (2022-01-01) -
Two-Step Matching Approach to Obtain More Control Points for SIFT-like Very-High-Resolution SAR Image Registration
by: Yang Deng, et al.
Published: (2023-04-01)