Learning group patterns for ground-based cloud classification in wireless sensor networks

Abstract

Cloud classification of ground-based images is a challenging task due to extreme variations under different atmospheric conditions. With the development of wireless sensor networks (WSN), it provides the possibility to understand and classify clouds more accurately. Recent research has focused on extracting discriminative cloud image features in WSN, which plays a crucial role in achieving competitive classification performance. In this paper, a novel feature extraction algorithm by learning group patterns in WSN is proposed for ground-based cloud classification. The proposed descriptors take texture resolution variations into account by cascading the salient local binary pattern (SLBP) information of hierarchical spatial pyramids. Through learning group patterns, we can obtain more useful information for cloud representation in WSN. Experimental results using ground-based cloud databases demonstrate that the proposed method can achieve better results than the current methods.