A Support Vector Machine-Based Particle Filter Method for Improved Flooding Classification
文献类型：期刊论文
| 作者 | Insom, Patcharin1; Cao, Chunxiang1; Boonsrimuang, Pisit1; Liu, Di1; Saokarn, Apitach1; Yomwan, Peera1; Xu, Yunfei1 |
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2015 |
| 卷号 | 12期号:9页码:414-425 |
| 关键词 | Flooding classification particle filter (PF) Radarsat support vector machine (SVM) |
| 通讯作者 | Insom, P (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China. |
| 英文摘要 | Support vector machines (SVMs) have been applied to land cover classification, and a number of studies have demonstrated their ability to increase classification accuracy. The high correlation between the data set and SVM training model parameters indicates the high performance of the classification model. To improve the correlation, research has focused on the integration of SVMs and other algorithms for data set selection and SVM training model parameter estimation. This letter proposes a novel method, based on a particle filter (PF), of estimating SVM training model parameters according to an observation system. By treating the SVM training function as the observation system of the PF, the new method automatically updates the SVM training model parameters to values that are more appropriate for the data set and can provide a better classification model than can the original model, wherein the parameters are set by trial and error. Various experiments were conducted using Radarsat-2 synthetic aperture radar data from the 2011 Thailand flood. The proposed method provides superior performance and a more accurate analysis compared with the standard SVM. |
| 研究领域[WOS] | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000359579000029 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38127]  |
| 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.[Insom, Patcharin 2.Cao, Chunxiang 3.Liu, Di 4.Saokarn, Apitach 5.Xu, Yunfei] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China 6.[Insom, Patcharin 7.Liu, Di 8.Saokarn, Apitach] Univ Chinese Acad Sci, Beijing 100094, Peoples R China 9.[Boonsrimuang, Pisit] King Mongkuts Inst Technol Ladkrabang, Fac Engn, Telecommun Engn Dept, Bangkok 10520, Thailand 10.[Saokarn, Apitach] Royal Thai Survey Dept, Bangkok 10520, Thailand |
| 推荐引用方式 GB/T 7714 | Insom, Patcharin,Cao, Chunxiang,Boonsrimuang, Pisit,et al. A Support Vector Machine-Based Particle Filter Method for Improved Flooding Classification[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2015,12(9):414-425. |
| APA | Insom, Patcharin.,Cao, Chunxiang.,Boonsrimuang, Pisit.,Liu, Di.,Saokarn, Apitach.,...&Xu, Yunfei.(2015).A Support Vector Machine-Based Particle Filter Method for Improved Flooding Classification.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,12(9),414-425. |
| MLA | Insom, Patcharin,et al."A Support Vector Machine-Based Particle Filter Method for Improved Flooding Classification".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 12.9(2015):414-425. |
入库方式： OAI收割
来源：遥感与数字地球研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。