12 July 2016 Feature significance-based multibag-of-visual-words model for remote sensing image scene classification
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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.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Lijun Zhao, Lijun Zhao, Ping Tang, Ping Tang, Lianzhi Huo, 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 . Submission:
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