Special Section on High-Performance Computing in Applied Remote Sensing

Parallel positive Boolean function approach to classification of remote sensing images

[+] Author Affiliations
Yang-Lang Chang

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

Tung-Ju Hsieh

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

Yen-Lin Chen

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

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

Jyh-Perng Fang

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

Bormin Huang

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

J. Appl. Remote Sens. 5(1), 051505 (December 01, 2011). doi:10.1117/1.3626866
History: Received May 05, 2011; Revised July 09, 2011; Accepted August 02, 2011; Published December 01, 2011; Online December 01, 2011
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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.

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

Citation

Yang-Lang Chang ; Tung-Ju Hsieh ; Antonio Plaza ; Yen-Lin Chen ; Wen-Yew Liang, et al.
"Parallel positive Boolean function approach to classification of remote sensing images", J. Appl. Remote Sens. 5(1), 051505 (December 01, 2011). ; http://dx.doi.org/10.1117/1.3626866


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