Multi-branch reverse attention semantic segmentation network for building extraction
Extraction of color and texture features of buildings from high-resolution remote sensing images often encounters the problems of interference of background information and varying target scales. In addition, most of the current attention mechanisms focus on building key feature selection for buildi...
Main Authors: | Wenxiang Jiang, Yan Chen, Xiaofeng Wang, Menglei Kang, Mengyuan Wang, Xuejun Zhang, Lixiang Xu, Cheng Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2024-03-01
|
Series: | Egyptian Journal of Remote Sensing and Space Sciences |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1110982323001035 |
Similar Items
-
AANet: Adaptive Attention Networks for Semantic Segmentation of High-Resolution Remote Sensing Imagery
by: Yan Chen, et al.
Published: (2024-01-01) -
RAAFNet: Reverse Attention Adaptive Fusion Network for Large-Scale Point Cloud Semantic Segmentation
by: Kai Wang, et al.
Published: (2024-08-01) -
Adaptive Local Cross-Channel Vector Pooling Attention Module for Semantic Segmentation of Remote Sensing Imagery
by: Xiaofeng Wang, et al.
Published: (2023-04-01) -
Hybrid Attention Fusion Embedded in Transformer for Remote Sensing Image Semantic Segmentation
by: Yan Chen, et al.
Published: (2024-01-01) -
LightFGCNet: A Lightweight and Focusing on Global Context Information Semantic Segmentation Network for Remote Sensing Imagery
by: Yan Chen, et al.
Published: (2022-12-01)