Learning group patterns for ground-based cloud classification in wireless sensor networks
© Liu and Zhang. 2016
Received: 19 September 2015
Accepted: 22 February 2016
Published: 1 March 2016
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.
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]. Most of cloud-related studies requires the technology of ground-based cloud observation, such as ground-based cloud classification [3, 4], 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) [8, 9], 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 [12, 13]. 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 [3, 9, 14]. 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.
In order to obtain the resolution information of cloud image in WSN, we learn the SLBP for each resolution, and then put all the patterns together to form the final representation. Specifically, we propose a novel feature extraction algorithm by learning group patterns (LGP) for ground-based cloud classification in WSN. The proposed descriptors take texture resolution variations into account by cascading the SLBP information of hierarchical spatial pyramids. Through learning group patterns, we can obtain more useful information for cloud representation in WSN.
The rest of this paper is organized as follows. In Section 2, the SLBP is briefly overviewed. and then the group patterns with pyramid representation is introduced in detail in Section 3. In Section 4, experimental results and discussions are given. Finally, conclusions are drawn in Section 5.
2 Brief review of SLBP
where p c represents the gray value of the central pixel, p n (n=0,⋯,N−1) denotes the gray value of the neighboring pixel on a circle of radius R, and N is the total number of neighbors. Suppose the coordinate of pc is (0,0), then the coordinates of p n are (R cos(2π n/N),R sin(2π n/N)). The gray values of neighbors that are not in the image grids can be calculated by interpolation. The step function s(x) is described with s(x)=1 if x≥0 and s(x)=0 otherwise. The minimum value in Eq. (1) denotes the label of the rotation invariant LBP at the central pixel.
Here, H[1,2,…] denotes the sorted histogram of all rotation invariant patterns, and T is a threshold determining the proportion of salient patterns. We empirically set T=80 %. The salient patterns of class i by solving Eq. (3) are denoted as S[i].
3 The proposed learning group patterns
In order to capture the hierarchical spatial pyramids information of cloud images in WSN, the proposed learning group patterns descriptors take texture resolution variations into account by cascading the SLBP information. Specifically, we learn the SLBP for each resolution, and then put all the patterns together to form the final representation. Pyramid transform is an effective multi-resolution analysis approach. In this paper, we represent a salient local binary pattern in a spatial pyramid domain.
where R x and R y are the down sampling ratios in x and y directions, respectively. R x R y >1 means down sampling is utilized during pyramid image generation, while R x =R y =1 means no sampling is utilized.
Through learning group patterns, we can obtain more useful information for cloud representation in WSN.
4 Experimental results and analysis
In this section, the proposed LGP is compared with the representative LBP , local ternary patterns (LTP) , DLBP  and SLBP  algorithms. To evaluate the effectiveness of our algorithm in WSN, a series of experiments are carried out. First, we will introduce two ground-based cloud databases captured in WSN: the Kiel database and the IapCAS-E database. Second, the experimental setup is described. Third, the experimental results in WSN on two databases are provided.
4.2 The experimental setup
where N C is the number of correctly classified cloud images in all seven classes. N is the total number of ground-based cloud images. Note that all the experimental results are computed based on Eq. 10. In each experiment, one fifth of the samples are randomly chosen from each class as training data while the remaining images are used for testing, and the process is repeated 100 times. The average accuracy over these 100 randomly splits is reported as the final results for reliability.
4.3 Results analysis
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 SLBP information of hierarchical spatial pyramids. Through learning group patterns in WSN, we can obtain more useful information for cloud representation. Compared to the conventional LBP descriptors and SLBP descriptors, the pyramid representation for local binary patterns shows its effectiveness. The experimental results show that our method achieves better results than previous ones on ground-based cloud classification in WSN.
This work is supported by the National Natural Science Foundation of China under Grant No. 61401309, and No. 61501327, Natural Science Foundation of Tianjin under Grant No. 15JCQNJC01700 and Doctoral Fund of Tianjin Normal University under Grant No. 5RL134 and No. 52XB1405.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- A Taravat, FF Del, C Cornaro, S Vergari, Neural networks and support vector machine algorithms for automatic cloud classification of whole-sky ground-based images. IEEE Geosci. Remote Sensing Lett.12(3), 666–670 (2014).View ArticleGoogle Scholar
- I Yanovsky, AB Davis, Separation of a cirrus layer and broken cumulus clouds in multispectral images. IEEE Trans. Geosci. Remote Sensing. 53(5), 2275–2285 (2015).View ArticleGoogle Scholar
- S Liu, C Wang, B Xiao, Z Zhang, X Cao, Tensor ensemble of ground-based cloud sequences: its modeling, classification and synthesis. IEEE Geosci. Remote Sensing Lett.10(5), 1190–1194 (2013).View ArticleGoogle Scholar
- A Kazantzidis, P Tzoumanikas, AF Bais, S Fotopoulos, G Economou, Cloud detection and classification with the use of whole-sky ground-based images. Atmos. Res.113:, 80–88 (2012).View ArticleGoogle Scholar
- J Yang, WT Lv, Y Ma, W Yao, QY Li, An automatic ground based cloud detection method based on adaptive threshold. J. Appl. Meteorological Sci.20(6), 713–721 (2013).Google Scholar
- U Feister, H Möller, T Sattler, J Shields, U Görsdorf, J Güldner, Comparison of macroscopic cloud data from ground-based measurements using vis/nir and ir instruments at Lindenberg, Germany. Atmos. Res.92(2), 395–407 (2010).View ArticleGoogle Scholar
- N Lamei, KN Hutchison, MM Crawford, N Khazenie, Cloudtype discrimination via multispectral textural analysis. Optical Eng.33(4), 1303–1313 (1994).View ArticleGoogle Scholar
- JE Shields, ME Karr, TP Tooman, in Proceedings of Eighth Atmospheric Radiation Measurement Science Team Meeting. The whole sky imager a year of progress, (1998), pp. 11–16.Google Scholar
- A Heinle, A Macke, A Srivastav, Automatic cloud classification of whole sky images. Atmos. Measurement Tech.3(1), 557–567 (2010).View ArticleGoogle Scholar
- CN Long, JM Sabburg, Calbó, J, D Pagès, Retrieving cloud characteristics from ground-based daytime color all-sky images. J. Atmos. Oceanic Technol.23(5), 633–652 (2006).View ArticleGoogle Scholar
- JA Shaw, B Thurairajah, in Proceedings of Thirteenth ARM Science Team Meeting. Short-term arctic cloud statistics at nsa from the infrared cloud imager, (2003).Google Scholar
- Q Liang, X Cheng, SC Huang, D Chen, Opportunistic sensing in wireless sensor networks: theory and application. IEEE Trans. Comput.63(8), 2002–2010 (2014).MathSciNetView ArticleGoogle Scholar
- Q Liang, Radar sensor wireless channel modeling in foliage environment: UWB versus narrowband. IEEE Sensors J.11(6), 1448–1457 (2011).View ArticleGoogle Scholar
- S Liu, C Wang, B Xiao, Z Zhang, Y Shao, Salient local binary pattern for ground-based cloud classification. Acta Meteorologica Sinica. 27(2), 211–220 (2013).View ArticleGoogle Scholar
- M Singh, M Glennen, Automated ground-based cloud recognition. Pattern Anal. Applic, 258–271 (2005).MathSciNetView ArticleGoogle Scholar
- J Calbó, J Sabburg, Feature extraction from whole-sky groundbased images for cloud-type recognition. J. Atmos. Ocean. Tech., 3–14 (2008).View ArticleGoogle Scholar
- W Zhuo, Z Cao, Y Xiao, Cloud classification of ground-based images using texture–structure features. J. Atmos. Ocean. Tech.31(1), 79–92 (2014).View ArticleGoogle Scholar
- T Ojala, M Pietikäinen, T Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell.24(7), 971–987 (2002).View ArticleMATHGoogle Scholar
- X Tan, B Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process.19(6), 1635–1650 (2010).MathSciNetView ArticleGoogle Scholar
- L Zheng, S Wang, Q Tian, Coupled binary embedding for large-scale image retrieval. IEEE Trans. Image Process.23(8), 3368–3380 (2014).MathSciNetView ArticleGoogle Scholar
- Z Guo, L Zhang, D Zhang, A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process.19(6), 1657–1663 (2010).MathSciNetView ArticleGoogle Scholar
- Z Zhang, C Wang, B Xiao, W Zhou, S Liu, Cross-view action recognition using contextual maximum margin clustering. IEEE Trans. Circuits Syst. Video Technol.24(10), 1663–1668 (2014).View ArticleGoogle Scholar
- L Zheng, S Wang, Z Liu, Q Tian, Fast image retrieval: query pruning and early termination. IEEE Trans. Multimedia. 17(5), 648–659 (2015).View ArticleGoogle Scholar
- Z Zhang, C Wang, B Xiao, W Zhou, S Liu, Attribute regularization based human action recognition. IEEE Trans. Inform. Forensics Secur.8(10), 1600–1609 (2013).View ArticleGoogle Scholar
- J Kalisch, A Macke, Estimation of the total cloud cover with high temporal resolution and parametrization of short-term fluctuations of sea surface insolation. Meteorologische Zeitschrift. 17(5), 603–611 (2008).View ArticleGoogle Scholar