Paper Info
Title
Pcnet: Cloud Detection In Fy-3d True-Color Imagery Using Multi-Scale Pyramid Contextual Information
Abstract
Cloud, one of the poor atmospheric conditions, significantly reduces the usability of optical remote-sensing data and hampers follow-up applications. Thus, the identification of cloud remains a priority for various remote-sensing activities, such as product retrieval, land-use/cover classification, object detection, and especially for change detection. However, the complexity of clouds themselves make it difficult to detect thin clouds and small isolated clouds. To accurately detect clouds in satellite imagery, we propose a novel neural network named the Pyramid Contextual Network (PCNet). Considering the limited applicability of a regular convolution kernel, we employed a Dilated Residual Block (DRB) to extend the receptive field of the network, which contains a dilated convolution and residual connection. To improve the detection ability for thin clouds, the proposed new model, pyramid contextual block (PCB), was used to generate global information at different scales. FengYun-3D MERSI-II remote-sensing images covering China with 14,165 x 24,659 pixels, acquired on 17 July 2019, are processed to conduct cloud-detection experiments. Experimental results show that the overall precision rates of the trained network reach 97.1% and the overall recall rates reach 93.2%, which performs better both in quantity and quality than U-Net, UNet++, UNet3+, PSPNet and DeepLabV3+.
Year
DOI
Venue
2021
10.3390/rs13183670
REMOTE SENSING
Keywords
DocType
Volume
cloud detection, FY-3D remote-sensing images, pyramid contextual, deep learning
Journal
13
Issue
Citations 
PageRank 
18
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Wangbin Li100.68
Kaimin Sun2215.40
Zhuotong Du300.34
Xiuqing Hu401.35
Wenzhuo Li5315.70
Jinjiang Wei600.68
Song Gao700.34