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Journal of Applied Remote Sensing / Volume 5 / Special Section on High-Performance Computing in Applied Remote Sensing

Parallel positive Boolean function approach to classification of remote sensing images

J. Appl. Remote Sens. 5, 051505 (Dec 01, 2011); http://dx.doi.org/10.1117/1.3626866

Yang-Lang Chang and Jyh-Perng Fang

National Taipei University of Technology, Department of Electrical Engineering, No. 1, Sec. 3, Chung-Hsiao E. Road, Taipei, 10608 Taiwan ylchang@ntut.edu.tw

Tung-Ju Hsieh, Yen-Lin Chen, and Wen-Yew Liang

National Taipei University of Technology, Department of Computer Science and Information Engineering, No. 1, Sec. 3, Chung-Hsiao E. Road, Taipei, 10608 Taiwan

Antonio Plaza

University of Extremadura, Department of Technology of Computers and Communications, Escuela Politecnica, 10003 Caceres, Spain

Bormin Huang

University of Wisconsin-Madison, Cooperative Institute for Meteorological Satellite, Studies Space Science and Engineering Center, Madison, Wisconsin 53706

We present a parallel image classification approach, referred to as the parallel positive Boolean function (PPBF), to multisource remote sensing images. PPBF is originally from the positive Boolean function (PBF) classifier scheme. The PBF multiclassifier is developed from a stack filter to classify specific classes of land covers. In order to enhance the efficiency of PBF, we propose PPBF to reduce the execution time using parallel computing techniques. PPBF fully utilizes the significant parallelism embedded in PBF to create a set of PBF stack filters on each parallel node based on different classes of land uses. It is implemented by combining the message-passing interface library and the open multiprocessing (OpenMP) application programing interface in a hybrid mode. The experimental results demonstrate that PPBF significantly reduces the computational loads of PBF classification.

© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

History
Received May 05, 2011
Accepted Aug 02, 2011
Revised Jul 09, 2011
Published online Dec 01, 2011
Digital Object Identifier
http://dx.doi.org/10.1117/1.3626866
Citation
Yang-Lang Chang, Tung-Ju Hsieh, Antonio Plaza, Yen-Lin Chen, Wen-Yew Liang, Jyh-Perng Fang and Bormin Huang, "Parallel positive Boolean function approach to classification of remote sensing images", J. Appl. Remote Sens. 5, 051505 (Dec 01, 2011); http://dx.doi.org/10.1117/1.3626866

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