DOI: 10.1016/j.atmosres.2018.02.023
Scopus记录号: 2-s2.0-85044060510
论文题名: Ground-based cloud classification by learning stable local binary patterns
作者: Wang Y. ; Shi C. ; Wang C. ; Xiao B.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 207 起始页码: 74
结束页码: 89
语种: 英语
英文关键词: Cloud classification
; Feature selection and extraction
; Local binary patterns
; Texture image
Scopus关键词: Deep neural networks
; Extraction
; Feature extraction
; Image processing
; Image texture
; Neural networks
; Statistical tests
; Classification accuracy
; Classification performance
; Cloud classification
; Deep convolutional neural networks
; Feature selection and extractions
; Local binary patterns
; Robust feature extractions
; Texture image
; Classification (of information)
; artificial neural network
; cloud classification
; database
; ground-based measurement
; image analysis
; pattern recognition
英文摘要: Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set. © 2018 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108891
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: State Key Laboratory of Management and Control for Complex Systems, Chinese Academy of Sciences, Institute of Automation, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100190, China; School of Software, Shanxi University, Taiyuan, 030006, China
Recommended Citation:
Wang Y.,Shi C.,Wang C.,et al. Ground-based cloud classification by learning stable local binary patterns[J]. Atmospheric Research,2018-01-01,207