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Journal of Applied Remote Sensing

Ground-based cloud classification using weighted local binary patterns
Author(s): Shuang Liu; Zhong Zhang; Xing Mei
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Paper Abstract

Ground-based cloud classification plays an essential role in meteorological research, and, recently, texture classification techniques have been introduced to automate the process. As a typical texture descriptor, local binary patterns (LBP) have emerged as a very powerful tool due to their effective representation ability. However, it neglects the local contrast information of ground-based cloud images, which may hinder the classification performance. We propose a descriptor called weighted local binary patterns (WLBP) for ground-based cloud classification. The proposed WLBP not only inherits the advantages of LBP but also encodes the useful contrast information of local structures. We define the variance of a local patch as a rotation invariant measure and use this measure as an adaptive weight to adjust the contribution of each neighboring pixel in the process of histogram accumulation. The experimental results demonstrate that the proposed WLBP achieves a better performance than the state-of-the-art methods.

Paper Details

Date Published: 27 August 2015
PDF: 11 pages
J. Appl. Remote Sens. 9(1) 095062 doi: 10.1117/1.JRS.9.095062
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Shuang Liu, Tianjin Normal Univ. (China)
Zhong Zhang, Tianjin Normal Univ. (China)
Xing Mei, Univ. at Albany (United States)


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