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RS_C11_Database
Abstract
11 scenes, all using high resolution remote sensing images, downloaded from Google Earth
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Content uploaded by Lijun Zhao
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... The second dataset ("dataset 2") was created by the authors and has been made available for other researchers, which can be downloaded at Ref. 41. This dataset was manually extracted from Google Earth, which covers the HRRS images of several US cities including Washington DC, Los Angeles, San Francisco, New York, San Diego, Chicago, and Houston. ...
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.
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