Learning Discriminative Salient LBP for Cloud Classification in Wireless Sensor Networks

We focus on the issue of ground-based cloud classification in wireless sensor networks (WSN) and propose a novel feature learning algorithm named discriminative salient local binary pattern (DSLBP) to tackle this issue. The proposed method is a two-layer model for learning discriminative patterns. The first layer is designed to learn the most salient and robust patterns from each class, and the second layer is used to obtain features with discriminative power and representation capability. Based on this strategy, discriminative patterns are obtained according to the characteristics of training cloud data from different sensor nodes, which can adapt variant cloud images. The experimental results show that the proposed algorithm achieves better results than other state-of-the-art cloud classification algorithms in WSN.

[1]  Josep Calbó,et al.  Feature Extraction from Whole-Sky Ground-Based Images for Cloud-Type Recognition , 2008 .

[2]  Shu Liao,et al.  Dominant Local Binary Patterns for Texture Classification , 2009, IEEE Transactions on Image Processing.

[3]  Chunheng Wang,et al.  Tensor Ensemble of Ground-Based Cloud Sequences: Its Modeling, Classification, and Synthesis , 2013, IEEE Geoscience and Remote Sensing Letters.

[4]  Paul W. Fieguth,et al.  Texture Classification from Random Features , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Qilian Liang,et al.  Radar Sensor Wireless Channel Modeling in Foliage Environment: UWB Versus Narrowband , 2011, IEEE Sensors Journal.

[6]  Maneesha Singh,et al.  Automated ground-based cloud recognition , 2005, Pattern Analysis and Applications.

[7]  Janet Shields,et al.  Daylight visible/NIR whole-sky imagers for cloud and radiance monitoring in support of UV research programs , 2003, SPIE Optics + Photonics.

[8]  Chunheng Wang,et al.  Attribute Regularization Based Human Action Recognition , 2013, IEEE Transactions on Information Forensics and Security.

[9]  Ernest M. Agee,et al.  A Revised Tornado Definition and Changes in Tornado Taxonomy , 2014 .

[10]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[12]  Josep Calbó,et al.  Retrieving Cloud Characteristics from Ground-Based Daytime Color All-Sky Images , 2006 .

[13]  Zhiguo Cao,et al.  Cloud Classification of Ground-Based Images Using Texture–Structure Features , 2014 .

[14]  Fabio Del Frate,et al.  Neural Networks and Support Vector Machine Algorithms for Automatic Cloud Classification of Whole-Sky Ground-Based Images , 2015, IEEE Geoscience and Remote Sensing Letters.

[15]  A. Heinle,et al.  Automatic cloud classification of whole sky images , 2010 .

[16]  Andreas Macke,et al.  Estimation of the total cloud cover with high temporal resolution and parametrization of short-term fluctuations of sea surface insolation , 2008 .

[17]  C. Long,et al.  Total Sky Imager Model 880 Status and Testing Results , 2001 .

[18]  Kenneth A. Buch,et al.  Cloud classification using whole-sky imager data , 1995 .

[19]  Lei Liu,et al.  Comparison of Cloud Properties from Ground-Based Infrared Cloud Measurement and Visual Observations , 2013 .

[20]  A Cazorla,et al.  Development of a sky imager for cloud cover assessment. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[21]  J. Shaw,et al.  Short-Term Arctic Cloud Statistics at NSA from the Infrared Cloud Imager , 2003 .

[22]  George Economou,et al.  Cloud detection and classification with the use of whole-sky ground-based images , 2012 .

[23]  Xiuzhen Cheng,et al.  Opportunistic Sensing in Wireless Sensor Networks: Theory and Application , 2014, IEEE Trans. Computers.