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

Shuang Liu,Zhong Zhang

doi:10.1186/s13638-016-0564-x

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.

Highlights

Clouds play an important role in the earth’s radiation budget because of their absorption and scattering of solar and infrared radiation, and their change is an important influence factor of climate change [1, 2]
We focus on cloud classification in wireless sensor networks (WSN)
4 Experimental results and analysis the proposed learning group patterns (LGP) is compared with the representative local binary pattern (LBP) [18], local ternary patterns (LTP) [19], dominant LBP (DLBP) [21] and salient local binary pattern (SLBP) [14] algorithms

Summary

Introduction

Clouds play an important role in the earth’s radiation budget because of their absorption and scattering of solar and infrared radiation, and their change is an important influence factor of climate change [1, 2]. Some work focuses on classification clouds based on satellite images [7]. Zhuo et al [17] proposed the color census transform to capture texture and color information for cloud classification.

Results

Conclusion