Cloud Detection of Remote Sensing Image Based on Multi-Scale Data and Dual-Channel Attention Mechanism

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 15; p. 3710
• Main Authors: Yan, Qing, Liu, Hu, Zhang, Jingjing, Sun, Xiaobing, Xiong, Wei, Zou, Mingmin, Xia, Yi, Xun, Lina
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Accuracy; Algorithms; attention mechanism; Automation; cloud detection; Clouds; Deep learning; Image segmentation; Information processing; Machine learning; multi-scale; Neural networks; Pixels; Remote sensing; Semantic segmentation; Semantics; Statistical analysis; Visual discrimination learning; Visual effects
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14153710

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.