Hierarchical Self-Attention Embedded Neural Network With Dense Connection for Remote-Sensing Image Semantic Segmentation
Semantic segmentation of remote-sensing imagery strives to assign a pixel-wise semantic label. Since encoder-decoder networks have demonstrated tremendous success in natural image semantic segmentation, the adoption and extension of this kind of method are transferring such superior performance for...
Main Authors: | Chunhua Li, Xin Li, Runliang Xia, Tao Li, Xin Lyu, Yao Tong, Liancheng Zhao, Xinyuan Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9535467/ |
Similar Items
-
Dense Aggregation Based Efficient Network for Image Semantic Segmentation in Edge Intelligent Tasks
by: Chaoxia Wu, et al.
Published: (2021-01-01) -
High-Resolution Aerial Imagery Semantic Labeling with Dense Pyramid Network
by: Xuran Pan, et al.
Published: (2018-11-01) -
Hybridizing Cross-Level Contextual and Attentive Representations for Remote Sensing Imagery Semantic Segmentation
by: Xin Li, et al.
Published: (2021-07-01) -
LASDU: A Large-Scale Aerial LiDAR Dataset for Semantic Labeling in Dense Urban Areas
by: Zhen Ye, et al.
Published: (2020-07-01) -
Attentively Learning Edge Distributions for Semantic Segmentation of Remote Sensing Imagery
by: Xin Li, et al.
Published: (2021-12-01)