Semantic Segmentation of Urban Buildings Using a High-Resolution Network (HRNet) with Channel and Spatial Attention Gates
In this study, building extraction in aerial images was performed using csAG-HRNet by applying HRNet-v2 in combination with channel and spatial attention gates. HRNet-v2 consists of transition and fusion processes based on subnetworks according to various resolutions. The channel and spatial attenti...
Main Authors: | Seonkyeong Seong, Jaewan Choi |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/16/3087 |
Similar Items
-
MAFF-HRNet: Multi-Attention Feature Fusion HRNet for Building Segmentation in Remote Sensing Images
by: Zhihao Che, et al.
Published: (2023-02-01) -
E-HRNet: Enhanced Semantic Segmentation Using Squeeze and Excitation
by: Jin-Seong Kim, et al.
Published: (2023-08-01) -
Integrating Gate and Attention Modules for High-Resolution Image Semantic Segmentation
by: Zixian Zheng, et al.
Published: (2021-01-01) -
Enhancing U-Net with Spatial-Channel Attention Gate for Abnormal Tissue Segmentation in Medical Imaging
by: Trinh Le Ba Khanh, et al.
Published: (2020-08-01) -
AGDF-Net: Attention-Gated and Direction-Field-Optimized Building Instance Extraction Network
by: Weizhi Liu, et al.
Published: (2023-07-01)