Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning

doi:10.3390/rs10010008

CASIA OpenIR > 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

	Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning
	Zhang, Zhong 1; Li, Donghong 1; Liu, Shuang 1; Xiao, Baihua2 ; Cao, Xiaozhong 3
发表期刊	REMOTE SENSING
	2018
卷号	10 期号:1
文章类型	Article
摘要	Cross-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.
关键词	Ground-based Cloud Classification Machine Learning Transfer Of Local Features Discriminative Metric Learning
WOS标题词	Science & Technology ; Technology
DOI	10.3390/rs10010008
关键词[WOS]	OBJECT RECOGNITION ; FEATURE-EXTRACTION ; IMAGES ; DISTANCE ; PATTERN ; CORTEX
收录类别	SCI
语种	英语
项目资助者	National 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研究方向	Remote Sensing
WOS类目	Remote Sensing
WOS记录号	WOS:000424092300007
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21949
专题	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
作者单位	1.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
推荐引用方式 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).