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 [, ]. Most of cloud-related studies requires the technology of ground-based cloud observation, such as ground-based cloud classification [, ], cloud cover evaluation (or cloud fraction) , and cloud height measure. Among, ground-based cloud classification has attracted much attention from the research community. It is because successful cloud classification can improve the precision of weather prediction and help us to understand climatic development . Clouds are currently studied using both satellites and ground-based weather stations. Some work focuses on classification clouds based on satellite images . However, the information extracted from large-scale satellite images fails to capture the details of cloud because these images generally possess low resolution. On the contrary, ground-based cloud observations are able to obtain richer, more accurate retrievals of cloud information. Nowadays, ground-based clouds are classified by the observers who are trained professionally. However, different observers will obtain discrepant classification results due to a different level of professional skills. Furthermore, this work is complicated and time-consuming. Hence, the technique of automatic ground-based cloud classification is a challenging task and is still under development.
The ground-based sky-imaging devices have been widely used for obtaining information on sky conditions. Typical devices, including WSI (whole sky imager) [, ], TSI (total sky imager) and ICI (infrared cloud imager) , can provide continuous sky images from which one can infer cloud macroscopic properties. Traditionally, the cloud classification techniques handle cloud images captured from only one image sensor.
Recently, wireless sensor networks (WSN) have attracted a lot of attention, particularly with the development of smart sensors [, ]. WSN can be applied in many fields including remote environmental monitoring and object classification. When each image sensor serves as a sensor node, WSN can be employed to classify clouds. In this paper, we focus on cloud classification in WSN.
Based on the above devices, a lot of methods have been proposed for ground-based cloud classification [, , ]. Singh and Glennen used co-occurrence matrix and autocorrelation to extract features from common digital images for cloud classification . Calbó and Sabburg applied statistical texture features and pattern features based on a Fourier spectrum to classify eight predefined sky conditions . Heinle et al. proposed an approach to extract spectral features and some simple textural features, such as energy and entropy for a fully automated classification algorithm, in which seven different sky conditions are distinguished . Zhuo et al. proposed the color census transform to capture texture and color information for cloud classification. Although these works are suggestive, many important problems for ground-based cloud classification have not yet been explored. For example, the extracted features are not discriminative enough to describe the ground-based cloud images, which might lead to poor classification performance.
Clouds can be thought of one kind of a natural texture, and it is reasonable for ground-based cloud images to be handled with texture classification methods. As one kind of classical texture descriptors, local binary pattern (LBP) [
] is particularly popular due to its simplicity and efficiency, and various extensions are made for the conventional LBP descriptors [
