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

Lijun Zhao; Ping Tang; Lianzhi Huo

doi:10.1117/1.JRS.10.035004

12 July 2016 Feature significance-based multibag-of-visual-words model for remote sensing image scene classification

Lijun Zhao, Ping Tang, Lianzhi Huo

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 3, 035004 (July 2016). https://doi.org/10.1117/1.JRS.10.035004

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.

Citation Download Citation

Lijun Zhao, Ping Tang, and Lianzhi Huo "Feature significance-based multibag-of-visual-words model for remote sensing image scene classification," Journal of Applied Remote Sensing 10(3), 035004 (12 July 2016). https://doi.org/10.1117/1.JRS.10.035004

Published: 12 July 2016

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