English  |  正體中文  |  简体中文  |  Items with full text/Total items : 28611/40649
Visitors : 649072      Online Users : 50
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28200

Title: Multisource image classification based on parallel minimum classification error learning
Authors: Yang-Lang Chang;Jyh-Perng Fang;Wen-Yew Liang;Lena Chang;Kun-Shan Chen
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Date: 2008-07-07
Issue Date: 2011-10-21T02:36:12Z
Publisher: Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Abstract: Abstract:In this paper we present a parallel classification learning method, referred to as parallel minimum classification error (PMCE) learning, for supervised classification of multisource remote sensing images. The approach is based on the positive Boolean function (PBF) classifier scheme. The PBF implements the minimum classification error (MCE) as a criterion to improve classification performance. By evenly distributing both positive and negative samples of MCE learning modules to different PMCE learning nodes, PMCE outperforms the original one in terms of execution time. It fully utilizes the significant parallelism embedded in MCE learning of PBF to create a set of PMCE learning nodes implemented by using the message passing interface (MPI) library and the open multi-processing (OpenMP) application programming interface. A sophisticated hierarchical structure of hybrid PMCE, which combines clusterbased MPI with multicore-based OpenMP, is proposed to demonstrate the flexibility of implementation of the proposed scheme. The effectiveness of the proposed PMCE is evaluated by fusing MODIS/ASTER airborne simulator (MASTER) hyperspectral images and the Airborne Synthetic Aperture Radar (AIRSAR) images for land cover classification during the Pacrim II campaign. The experimental results demonstrated that PMCE can improve the computational speed of PBF classification significantly.
Relation: 3, pp.443-446
URI: http://ntour.ntou.edu.tw/handle/987654321/28200
Appears in Collections:[通訊與導航工程學系] 演講及研討會

Files in This Item:

File Description SizeFormat

All items in NTOUR are protected by copyright, with all rights reserved.


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback