Information integration for ground-based cloud classification using joint consistent sparse coding in heterogeneous sensor network

Abstract

Although sparsity-based algorithm has emerged as an extremely powerful tool for information integration, it neglects the relationship of heterogeneous features and coding coefficients from the same class in the training stage, which may cause declining of the classification performance. In this paper, we focus on information integration for ground-based cloud classification in heterogeneous sensor network (HSN), and propose a novel coding strategy named joint consistent sparse coding (JCSC) to overcome the drawbacks of traditional sparse coding. In order to integrate information effectively, we add a joint sparse regularization to learn the relationship of heterogeneous features. Moreover, we utilize the consistent constraint on coding coefficients. In this way, coding coefficients from the same class can be forced to their mean vector, and therefore they are more compact and discriminative. The experimental results demonstrate that our method achieves better performance than the state-of-the-art methods.