Ground-based cloud classification using weighted local binary patterns

Shuang Liu; Zhong Zhang; Xing Mei

doi:10.1117/1.JRS.9.095062

27 August 2015 Ground-based cloud classification using weighted local binary patterns

Shuang Liu, Zhong Zhang, Xing Mei

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 095062 (August 2015). https://doi.org/10.1117/1.JRS.9.095062

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.

Citation Download Citation

Shuang Liu, Zhong Zhang, and Xing Mei "Ground-based cloud classification using weighted local binary patterns," Journal of Applied Remote Sensing 9(1), 095062 (27 August 2015). https://doi.org/10.1117/1.JRS.9.095062

Published: 27 August 2015

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