Paper Info
Title
Deep tensor fusion network for multimodal ground-based cloud classification in weather station networks.
Abstract
Accurate multimodal ground-based cloud classification in weather station networks is a challenging task, because the existing methods fuse cloud visual data and multimodal data at the vector level resulting in the spatial information loss. In this work, we propose a method named deep tensor fusion network (DTFN) for multimodal ground-based cloud classification in weather station networks, which could learn completed cloud information by fusing heterogeneous features at the tensor level in a unified framework. The DTFN is composed of the visual tensor subnetwork (VTN) and the multimodal tensor subnetwork (MTN). The VTN transforms cloud images into cloud visual tensors using a deep network and therefore the spatial information of ground-based cloud images can be maintained. Meanwhile, the MTN is designed as a couple of deconvolutional layers in order to transform the multimodal data into multimodal tensors and ensure the multimodal tensors to be mathematically compatible with cloud visual tensors. Furthermore, to fuse cloud visual tensor and multimodal tensor, we propose the tensor fusion layer to exploit the high-order correlations between them. The DTFN is evaluated on MGCD and exceeds the state-of-the-art methods, which validates its effectiveness for multimodal ground-based cloud classification in weather station networks.
Year
DOI
Venue
2020
10.1016/j.adhoc.2019.101991
Ad Hoc Networks
Keywords
Field
DocType
Weather station networks,Convolution neural network,Ground-based cloud classification
Spatial analysis,Data mining,Tensor,Compatibility (mechanics),Weather station,Computer science,Exploit,Fuse (electrical),Subnetwork,Distributed computing,Cloud computing
Journal
Volume
ISSN
Citations 
96
1570-8705
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Mei Li1211.53
Shuang Liu23622.95
Zhong Zhang314132.42