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DOI | 10.1016/j.atmosres.2018.02.023 |
Ground-based cloud classification by learning stable local binary patterns | |
Wang, Yu1,2,3; Shi, Cunzhao1; Wang, Chunheng1; Xiao, Baihua1 | |
2018-07-15 | |
发表期刊 | ATMOSPHERIC RESEARCH |
ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2018 |
卷号 | 207页码:74-89 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
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. |
英文关键词 | Local binary patterns Cloud classification Feature selection and extraction Texture image |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000430901800006 |
WOS关键词 | INVARIANT TEXTURE CLASSIFICATION ; CEILOMETER MEASUREMENTS ; SOLAR IRRADIANCE ; FACE RECOGNITION ; TROPICAL REGION ; IMAGE FEATURES ; SKY IMAGES ; COVER ; SEGMENTATION ; ALGORITHMS |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/38309 |
专题 | 地球科学 |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China; 3.Shanxi Univ, Sch Software, Taiyuan 030006, Shanxi, Peoples R China |
推荐引用方式 GB/T 7714 | Wang, Yu,Shi, Cunzhao,Wang, Chunheng,et al. Ground-based cloud classification by learning stable local binary patterns[J]. ATMOSPHERIC RESEARCH,2018,207:74-89. |
APA | Wang, Yu,Shi, Cunzhao,Wang, Chunheng,&Xiao, Baihua.(2018).Ground-based cloud classification by learning stable local binary patterns.ATMOSPHERIC RESEARCH,207,74-89. |
MLA | Wang, Yu,et al."Ground-based cloud classification by learning stable local binary patterns".ATMOSPHERIC RESEARCH 207(2018):74-89. |
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