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Journal of Applied Remote Sensing

Feature significance-based multibag-of-visual-words model for remote sensing image scene classification
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Paper Abstract

To obtain a complete representation of scene information in high spatial resolution remote sensing scene images, an increasing number of studies have begun to pay attention to the multiple low-level feature types-based bag-of-visual-words (multi-BOVW) model, for which the two-phase classification-based multi-BOVW method is one of the most popular approaches. However, this method ignores the information of feature significance among different feature types in the score-level fusion stage, thus affecting the classification performance of the multi-BOVW methods. To address this limitation, a feature significance-based multi-BOVW scene classification method was proposed, which integrates the information of feature separating capabilities among different scene categories into the traditional two-phase classification-based score-level fusion framework, realizing different treatments for different feature channels in classifying different scene categories. Experimental results show that the proposed method outperforms the traditional score-level fusion-based multi-BOVW methods and effectively explores the feature significance information in multiclass remote sensing image scene classification tasks.

Paper Details

Date Published: 12 July 2016
PDF: 21 pages
J. Appl. Remote Sens. 10(3) 035004 doi: 10.1117/1.JRS.10.035004
Published in: Journal of Applied Remote Sensing Volume 10, Issue 3
Show Author Affiliations
Lijun Zhao, Institute of Remote Sensing and Digital Earth (China)
Ping Tang, Institute of Remote Sensing and Digital Earth (China)
Lianzhi Huo, Institute of Remote Sensing and Digital Earth (China)


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