We focus on the issue of ground-based cloud classification in wireless sensor networks (WSN) and propose a novel feature learning algorithm named discriminative salient local binary pattern (DSLBP) to tackle this issue. The proposed method is a two-layer model for learning discriminative patterns. The first layer is designed to learn the most salient and robust patterns from each class, and the second layer is used to obtain features with discriminative power and representation capability. Based on this strategy, discriminative patterns are obtained according to the characteristics of training cloud data from different sensor nodes, which can adapt variant cloud images. The experimental results show that the proposed algorithm achieves better results than other state-of-the-art cloud classification algorithms in WSN.
['Shuang Liu', 'Zhong Zhang']
Learning discriminative salient LBP for cloud classification in wireless sensor networks