中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 复杂系统管理与控制国家重点实验室
  5. 影像分析与机器视觉团队
Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition

文献类型：期刊论文

作者Liu, Shuang2; Li, Mei2; Zhang, Zhong2; Xiao, Baihua3; Durrani, Tariq S.1
刊名REMOTE SENSING
出版日期2020-02-01
卷号12期号:3页码:20
关键词ground-based cloud recognition convolution neural network feature fusion
DOI10.3390/rs12030464
通讯作者Zhang, Zhong(zhangz@tjnu.edu.cn)
英文摘要In recent times, deep neural networks have drawn much attention in ground-based cloud recognition. Yet such kind of approaches simply center upon learning global features from visual information, which causes incomplete representations for ground-based clouds. In this paper, we propose a novel method named multi-evidence and multi-modal fusion network (MMFN) for ground-based cloud recognition, which could learn extended cloud information by fusing heterogeneous features in a unified framework. Namely, MMFN exploits multiple pieces of evidence, i.e., global and local visual features, from ground-based cloud images using the main network and the attentive network. In the attentive network, local visual features are extracted from attentive maps which are obtained by refining salient patterns from convolutional activation maps. Meanwhile, the multi-modal network in MMFN learns multi-modal features for ground-based cloud. To fully fuse the multi-modal and multi-evidence visual features, we design two fusion layers in MMFN to incorporate multi-modal features with global and local visual features, respectively. Furthermore, we release the first multi-modal ground-based cloud dataset named MGCD which not only contains the ground-based cloud images but also contains the multi-modal information corresponding to each cloud image. The MMFN is evaluated on MGCD and achieves a classification accuracy of 88.63% comparative to the state-of-the-art methods, which validates its effectiveness for ground-based cloud recognition.
WOS关键词LOCAL BINARY PATTERN ; FEATURE-EXTRACTION ; CLASSIFICATION ; FEATURES ; IMAGES ; SCALE
资助项目National Natural Science Foundation of China[61711530240] ; Natural Science Foundation of Tianjin[19JCZDJC31500] ; Fund of Tianjin Normal University[135202RC1703] ; Open Projects Program of National Laboratory of Pattern Recognition[202000002] ; Tianjin Higher Education Creative Team Funds Program
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000515393800123
出版者MDPI
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Tianjin ; Fund of Tianjin Normal University ; Open Projects Program of National Laboratory of Pattern Recognition ; Tianjin Higher Education Creative Team Funds Program
源URL[http://ir.ia.ac.cn/handle/173211/38328]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_影像分析与机器视觉团队
通讯作者Zhang, Zhong
作者单位1.Univ Strathclyde, Dept Elect & Elect Engn, Glasgow G1 1XQ, Lanark, Scotland
2.Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tr, Tianjin 300387, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Shuang,Li, Mei,Zhang, Zhong,et al. Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition[J]. REMOTE SENSING,2020,12(3):20.
APA Liu, Shuang,Li, Mei,Zhang, Zhong,Xiao, Baihua,&Durrani, Tariq S..(2020).Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition.REMOTE SENSING,12(3),20.
MLA Liu, Shuang,et al."Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition".REMOTE SENSING 12.3(2020):20.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。