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Semantic segmentation of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3+.
- Source :
- PLoS ONE; 1/20/2023, Vol. 17 Issue 1, p1-16, 16p
- Publication Year :
- 2023
-
Abstract
- Deeplabv3+ currently is the most representative semantic segmentation model. However, Deeplabv3+ tends to ignore targets of small size and usually fails to identify precise segmentation boundaries in the UAV remote sensing image segmentation task. To handle these problems, this paper proposes a semantic segmentation algorithm of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3+ (EMNet). EMNet uses MobileNetV2 as its backbone and adds an edge detection branch in the encoder to provide edge information for semantic segmentation. In the decoder, a multi-level upsampling method is designed to retain high-level semantic information (e.g., the target's location and boundary information). The experimental results show that the mIoU and mPA of EMNet improved over Deeplabv3+ by 7.11% and 6.93% on the dataset UAVid, and by 0.52% and 0.22% on the dataset ISPRS Vaihingen. [ABSTRACT FROM AUTHOR]
- Subjects :
- REMOTE sensing
DRONE aircraft
IMAGE segmentation
SPINE
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 17
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 161419412
- Full Text :
- https://doi.org/10.1371/journal.pone.0279097