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Journal of Applied Remote Sensing

Parallel positive Boolean function approach to classification of remote sensing images
Author(s): Yang-Lang Chang; Tung-Ju Hsieh; Antonio J. Plaza; Yen-Lin Chen; Wen-Yew Liang; Jyh-Perng Fang; Bormin Huang
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Paper Abstract

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.

Paper Details

Date Published: 1 January 2011
PDF: 16 pages
J. Appl. Rem. Sens. 5(1) 051505 doi: 10.1117/1.3626866
Published in: Journal of Applied Remote Sensing Volume 5, Issue 1
Show Author Affiliations
Yang-Lang Chang, National Taipei Univ. of Technology (Taiwan)
Tung-Ju Hsieh, National Taipei Univ. of Technology (Taiwan)
Antonio J. Plaza, Univ. de Extremadura (Spain)
Yen-Lin Chen, National Taipei Univ. of Technology (Taiwan)
Wen-Yew Liang, National Taipei Univ. of Technology (Taiwan)
Jyh-Perng Fang, National Taipei Univ. of Technology (Taiwan)
Bormin Huang, Univ. of Wisconsin-Madison (United States)

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