Joint Encoding LBP Features from Infrared and Visible-light Cloud Image Observations for Ground-based Cloud Classification
Wang Y(王钰); Wang CH(王春恒); Shi CZ(史存召); Xiao BH(肖柏华)
2018
Conference Name2018 IEEE International Geoscience and Remote Sensing Symposium
Conference Date20180722-20180727
Conference PlaceSpain
Abstract

Cloud type classification based on ground-based cloud image observations is an important task in atmospheric research. Currently, two kinds of cloud image observations with infrared and visible light images are widely used for cloud classification. However, they are only independently analyzed and simply compared in the current study. The useful information from these two kinds of images is not fully utilized and integrated. The classification performance could be improved if taking full advantage of the complementary information of these two observations. Thus, first, a database containing these two kinds of cloud images with same temporal resolution is released in this study. Then, a two-observation joint encoding strategy of LBP (local binary pattern) features is proposed to implement cloud classification by encoding the joint distribution of LBP patterns in different observations, which captures the correlation between two observations. Experimental results based on this database show the significant superiority of the proposed method compared to the results based on the single observation.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23676
Collection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
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang Y,Wang CH,Shi CZ,et al. Joint Encoding LBP Features from Infrared and Visible-light Cloud Image Observations for Ground-based Cloud Classification[C],2018.
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