Visual attention model based mining area recognition on massive high-resolution remote sensing images
文献类型：期刊论文
| 作者 | Song, Xiaolu1; He, Guojin1; Zhang, Zhaoming1; Long, Tengfei1; Peng, Yan1; Wang, Zhihua1 |
| 刊名 | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
 |
| 出版日期 | 2015 |
| 卷号 | 18期号:2(SI)页码:4534-4543 |
| 关键词 | Visual attention model High-resolution remote sensing Object extraction Snake model |
| 通讯作者 | He, GJ (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China. |
| 英文摘要 | With the development of remote sensing technology, satellite images with the characteristics of multi-scale, multi-band, and multi-date make it tend to be big data. So how to raise the extraction speed, precision and automatic degree of salient objects from high-resolution remote sensing images become urgent problems. Based on the analysis using an Itti visual attention model for natural image processing, we achieved improvements in two aspects: (1) the selection of salient regions based on elevation data, and (2) the segmentation of salient regions using the Snake model for precise object contour extraction. Tests on the extraction of 2.5 m high-resolution remote sensing image data in the rare earth mining area in Dingnan County, Jiangxi Province showed a false alarm rate of 14.8% and a missing alarm rate of 8.4% in the extraction of mine quantity data. The proposed method could be useful for improving the speed, precision and automatic extraction of salient objects from high-resolution remote sensing images as well as the boundary information of salient objects that are based on a visual attention model. |
| 研究领域[WOS] | Computer Science, Information Systems ; Computer Science, Theory & Methods |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000354412400005 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38199]  |
| 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.[Song, Xiaolu 2.He, Guojin 3.Zhang, Zhaoming 4.Long, Tengfei 5.Peng, Yan 6.Wang, Zhihua] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China 7.[Song, Xiaolu] Univ Chinese Acad Sci, Beijing 100039, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Xiaolu,He, Guojin,Zhang, Zhaoming,et al. Visual attention model based mining area recognition on massive high-resolution remote sensing images[J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS,2015,18(2(SI)):4534-4543. |
| APA | Song, Xiaolu,He, Guojin,Zhang, Zhaoming,Long, Tengfei,Peng, Yan,&Wang, Zhihua.(2015).Visual attention model based mining area recognition on massive high-resolution remote sensing images.CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS,18(2(SI)),4534-4543. |
| MLA | Song, Xiaolu,et al."Visual attention model based mining area recognition on massive high-resolution remote sensing images".CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 18.2(SI)(2015):4534-4543. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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