Attention-Gate-Based Encoder–Decoder Network for Automatical Building Extraction
Rapidly developing remote sensing technology provides massive data for urban planning, mapping, and disaster management. As a carrier of human productive activities, buildings are essential to both urban dynamic monitoring and suburban construction inspection. Fully-convolutional-network-based metho...
Main Authors: | Wenjing Deng, Qian Shi, Jun Li |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9351600/ |
Similar Items
-
Hippocampal subfields segmentation in brain MR images using generative adversarial networks
by: Yonggang Shi, et al.
Published: (2019-01-01) -
BAS<inline-formula><tex-math notation="LaTeX">$^{4}$</tex-math></inline-formula>Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images
by: Xian Sun, et al.
Published: (2020-01-01) -
Multiscale Attention Gated Network (MAGNet) for Retinal Layer and Macular Cystoid Edema Segmentation
by: Alex Cazanas-Gordon, et al.
Published: (2022-01-01) -
SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation
by: Guanzhou Chen, et al.
Published: (2021-12-01) -
Automatic Building Extraction From High-Resolution Aerial Imagery via Fully Convolutional Encoder-Decoder Network With Non-Local Block
by: Shengsheng Wang, et al.
Published: (2020-01-01)