Next Article in Journal
Predicting the Volume of Response to Tweets Posted by a Single Twitter Account
Next Article in Special Issue
Braille Recognition for Reducing Asymmetric Communication between the Blind and Non-Blind
Previous Article in Journal
Parking Site Selection for Light Rail Stations in Muaeng District, Khon Kaen, Thailand
Open AccessArticle

Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network

by Yanan Guo 1,2, Xiaoqun Cao 1,2,*, Bainian Liu 1,2 and Mei Gao 1,2
1
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
2
College of Computer, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(6), 1056; https://doi.org/10.3390/sym12061056
Received: 7 June 2020 / Revised: 21 June 2020 / Accepted: 23 June 2020 / Published: 25 June 2020
(This article belongs to the Special Issue Symmetry in Artificial Visual Perception and Its Application)
Cloud detection is an important and difficult task in the pre-processing of satellite remote sensing data. The results of traditional cloud detection methods are often unsatisfactory in complex environments or the presence of various noise disturbances. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in many fields such as image processing, speech recognition, autonomous driving, etc. This study proposes a deep learning model suitable for cloud detection, Cloud-AttU, which is based on a U-Net network and incorporates an attention mechanism. The Cloud-AttU model adopts the symmetric Encoder-Decoder structure, which achieves the fusion of high-level features and low-level features through the skip-connection operation, making the output results contain richer multi-scale information. This symmetrical network structure is concise and stable, significantly enhancing the effect of image segmentation. Based on the characteristics of cloud detection, the model is improved by introducing an attention mechanism that allows model to learn more effective features and distinguish between cloud and non-cloud pixels more accurately. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method. The proposed method is also able to achieve great results in the presence of snow/ice disturbance and other bright non-cloud objects, with strong resistance to disturbance. The Cloud-AttU model proposed in this study has achieved excellent results in the cloud detection tasks, indicating that this symmetric network architecture has great potential for application in satellite image processing and deserves further research. View Full-Text
Keywords: cloud detection; remote sensing images; U-Net architecture; attention mechanism; deep learning; convolutional neural network cloud detection; remote sensing images; U-Net architecture; attention mechanism; deep learning; convolutional neural network
Show Figures

Figure 1

MDPI and ACS Style

Guo, Y.; Cao, X.; Liu, B.; Gao, M. Cloud Detection for Satellite Imagery Using Attention-Based U-Net Convolutional Neural Network. Symmetry 2020, 12, 1056.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop