Feature significance-based multibag-of-visual-words model for remote sensing image scene classification
文献类型：期刊论文
| 作者 | Zhao, Lijun1; Tang, Ping1; Huo, Lianzhi1 |
| 刊名 | Journal of Applied Remote Sensing
 |
| 出版日期 | 2016 |
| 卷号 | 10期号:3 |
| 关键词 | PHOTOCHEMICAL REFLECTANCE INDEX VEGETATION INDEXES CHLOROPHYLL CONTENT PRECISION AGRICULTURE SPECTRAL REFLECTANCE PIGMENT COMPOSITION REMOTE ESTIMATION NITROGEN-CONTENT LEAF SENESCENCE BROAD-BAND |
| 通讯作者 | Zhao, Lijun (zhaolj01@radi.ac.cn) |
| 英文摘要 | 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). |
| 学科主题 | Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology |
| 类目[WOS] | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20163002634427 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39376]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 20 Datun Road, Chaoyang District, Beijing 2.100101, China |
| 推荐引用方式 GB/T 7714 | Zhao, Lijun,Tang, Ping,Huo, Lianzhi. Feature significance-based multibag-of-visual-words model for remote sensing image scene classification[J]. Journal of Applied Remote Sensing,2016,10(3). |
| APA | Zhao, Lijun,Tang, Ping,&Huo, Lianzhi.(2016).Feature significance-based multibag-of-visual-words model for remote sensing image scene classification.Journal of Applied Remote Sensing,10(3). |
| MLA | Zhao, Lijun,et al."Feature significance-based multibag-of-visual-words model for remote sensing image scene classification".Journal of Applied Remote Sensing 10.3(2016). |
入库方式： OAI收割
来源：遥感与数字地球研究所
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