Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning
Zhang, Zhong1; Li, Donghong1; Liu, Shuang1; Xiao, Baihua2; Cao, Xiaozhong3
Source PublicationREMOTE SENSING
2018
Volume10Issue:1
SubtypeArticle
AbstractCross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud classification are feature representation and similarity measurement. In this paper, we propose an effective feature representation called transfer of local features (TLF), and measurement method called discriminative metric learning (DML). The TLF is a generalized representation framework that can integrate various kinds of local features, e.g., local binary patterns (LBP), local ternary patterns (LTP) and completed LBP (CLBP). In order to handle domain shift, such as variations of illumination, image resolution, capturing location, occlusion and so on, the TLF mines the maximum response in regions to make a stable representation for domain variations. We also propose to learn a discriminant metric, simultaneously. We make use of sample pairs and the relationship among cloud classes to learn the distance metric. Furthermore, in order to improve the practicability of the proposed method, we replace the original cloud images with the convolutional activation maps which are then applied to TLF and DML. The proposed method has been validated on three cloud databases which are collected in China alone, provided by Chinese Academy of Meteorological Sciences (CAMS), Meteorological Observation Centre (MOC), and Institute of Atmospheric Physics (IAP). The classification accuracies outperform the state-of-the-art methods.
KeywordGround-based Cloud Classification Machine Learning Transfer Of Local Features Discriminative Metric Learning
WOS HeadingsScience & Technology ; Technology
DOI10.3390/rs10010008
WOS KeywordOBJECT RECOGNITION ; FEATURE-EXTRACTION ; IMAGES ; DISTANCE ; PATTERN ; CORTEX
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61501327 ; Natural Science Foundation of Tianjin(17JCZDJC30600 ; Fund of Tianjin Normal University(135202RC1703) ; Open Projects Program of National Laboratory of Pattern Recognition(201700001) ; 61711530240 ; 15JCQNJC01700) ; 61401309)
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000424092300007
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21949
Collection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
Affiliation1.Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tr, Tianjin 300387, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Intelligent Control Co, Beijing 100190, Peoples R China
3.China Meteorol Adm, Meteorol Observat Ctr, Beijing 100081, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Zhong,Li, Donghong,Liu, Shuang,et al. Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning[J]. REMOTE SENSING,2018,10(1).
APA Zhang, Zhong,Li, Donghong,Liu, Shuang,Xiao, Baihua,&Cao, Xiaozhong.(2018).Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning.REMOTE SENSING,10(1).
MLA Zhang, Zhong,et al."Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning".REMOTE SENSING 10.1(2018).
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