MSST-Net: A Multi-Scale Adaptive Network for Building Extraction from Remote Sensing Images Based on Swin Transformer
The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation. However, the segmentation model based on a convolutional neural network cannot capture the global features very well. A transformer, whose self-attention mechanism can su...
Main Authors: | Wei Yuan, Wenbo Xu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-11-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/13/23/4743 |
Similar Items
-
STransFuse: Fusing Swin Transformer and Convolutional Neural Network for Remote Sensing Image Semantic Segmentation
by: Liang Gao, et al.
Published: (2021-01-01) -
ACTNet: A Dual-Attention Adapter with a CNN-Transformer Network for the Semantic Segmentation of Remote Sensing Imagery
by: Zheng Zhang, et al.
Published: (2023-04-01) -
Swin Transformer Embedding Dual-Stream for Semantic Segmentation of Remote Sensing Imagery
by: Xuanyu Zhou, et al.
Published: (2024-01-01) -
A Semantic Segmentation Method for Remote Sensing Images Based on the Swin Transformer Fusion Gabor Filter
by: Dongdong Feng, et al.
Published: (2022-01-01) -
Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer
by: Weitao Li, et al.
Published: (2022-10-01)