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Cloud Detection of Remote Sensing Image Based on Multi-Scale Data and Dual-Channel Attention Mechanism.
- Source :
- Remote Sensing; Aug2022, Vol. 14 Issue 15, p3710-3710, 14p
- Publication Year :
- 2022
-
Abstract
- Cloud detection is one of the critical tasks in remote sensing image preprocessing. Remote sensing images usually contain multi-dimensional information, which is not utilized entirely in existing deep learning methods. This paper proposes a novel cloud detection algorithm based on multi-scale input and dual-channel attention mechanisms. Firstly, we remodeled the original data to a multi-scale layout in terms of channels and bands. Then, we introduced the dual-channel attention mechanism into the existing semantic segmentation network, to focus on both band information and angle information based on the reconstructed multi-scale data. Finally, a multi-scale fusion strategy was introduced to combine band information and angle information simultaneously. Overall, in the experiments undertaken in this paper, the proposed method achieved a pixel accuracy of 92.66% and a category pixel accuracy of 92.51%. For cloud detection, the proposed method achieved a recall of 97.76% and an F1 of 95.06%. The intersection over union (IoU) of the proposed method was 89.63%. Both in terms of quantitative results and visual effects, the deep learning model we propose is superior to the existing semantic segmentation methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- REMOTE sensing
DEEP learning
DATABASES
MACHINE learning
IMAGE segmentation
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 14
- Issue :
- 15
- Database :
- Complementary Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 158523769
- Full Text :
- https://doi.org/10.3390/rs14153710