Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning
文献类型：期刊论文
| 作者 | Zhang, Zhong1; Li, Donghong1; Liu, Shuang1; Xiao, Baihua2 ; Cao, Xiaozhong3
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2018 |
| 卷号 | 10期号:1 |
| 关键词 | Ground-based Cloud Classification Machine Learning Transfer Of Local Features Discriminative Metric Learning |
| DOI | 10.3390/rs10010008 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | OBJECT RECOGNITION ; FEATURE-EXTRACTION ; IMAGES ; DISTANCE ; PATTERN ; CORTEX |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000424092300007 |
| 资助机构 | 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) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21949]  |
| 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 |
| 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). |
入库方式： OAI收割
来源：自动化研究所
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