Title: Ground-based Cloud Classification by Learning Stable Local Binary Patterns
Authors: Wang, Y; Shi, CZ; Wang, CH; Xiao, BH
Author Full Names: Wang, Yu; Shi, Cunzhao; Wang, Chunheng; Xiao, Baihua
Source: ATMOSPHERIC RESEARCH, 207 74-89; 10.1016/j.atmosres.2018.02.023 JUL 15 2018
Language: English
Abstract: 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.
ISSN: 0169-8095
eISSN: 1873-2895
IDS Number: GE0KD
Unique ID: WOS:000430901800006