Comparing Pixel-Based Random Forest and the Object-Based Support Vector Machine Approaches to Map the Quasi-Circular Vegetation Patches Using Individual Seasonal Fused GF-1 Imagery
文献类型：期刊论文
| 作者 | Shi, Lei1; Liu, Qingsheng1,2; Huang, Chong1; Li, He1; Liu, Gaohuan1 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2020 |
| 卷号 | 8页码:228955-228966 |
| ISSN号 | 2169-3536 |
| 关键词 | Vegetation mapping Support vector machines Radio frequency Spatial resolution Satellites Vegetation Remote sensing Gaofen 1 satellite imagery objected-based support vector machine pixel-based random forest quasi-circular vegetation patch season influence |
| DOI | 10.1109/ACCESS.2020.3045057 |
| 通讯作者 | Liu, Qingsheng(liuqs@lreis.ac.cn) ; Huang, Chong(huangch@lreis.ac.cn) |
| 英文摘要 | The seasonal effect on land cover classification has been widely recognized. It is important to use the imagery acquired at key points of vegetation phenological development to obtain a higher classification accuracy for land cover. This study compared the effect of seasons on landscape classification and the quasi-circular vegetation patches (QVPs) detection from four fused Gaofen 1 images acquired in the different seasons by using the pixel-based random forest (RF) and object-based support vector machine (SVM) methods over the Yellow River Delta, China. The results from this study demonstrated that the seasonal effect on classifying landscapes and detecting the QVPs is significant, especially for the pixel-based RF method. The object-based SVM method was more appropriate for classifying landscape from the non-growing season images, while the pixel-based RF approach was more suitable for classifying the growing-season images. The spring data (April imagery; overall accuracy = 99.8%) and the winter data (February imagery; F measure = 65.9%) yielded the best results for landscape classification and QVP detection, respectively, by using the object-based SVM approach. Therefore, in practice, we recommend the use of February to April imagery with the object-based SVM approach to map the QVPs in the future. |
| WOS关键词 | TREE SPECIES CLASSIFICATION ; HIGH-SPATIAL-RESOLUTION ; WORLDVIEW-2 IMAGERY ; IKONOS IMAGERY ; SPOT 5 ; ENCROACHMENT ; SEGMENTATION ; SALTCEDAR ; DYNAMICS ; PATTERNS |
| 资助项目 | National Natural Science Foundation of China[41671422] ; National Natural Science Foundation of China[4151144012] ; National Natural Science Foundation of China[41661144030] ; National Key Research and Development Program of China[2016YFC1402701] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20030302] ; Innovation Project of the State Key Laboratory of Resources and Environmental Information System (LREIS)[088RA20CYA] ; Innovation Project of the State Key Laboratory of Resources and Environmental Information System (LREIS)[08R8A010YA] ; National Mountain Flood Disaster Investigation Project[SHZH-IWHR-57] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000604554100001 |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; Innovation Project of the State Key Laboratory of Resources and Environmental Information System (LREIS) ; National Mountain Flood Disaster Investigation Project |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/136583]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Liu, Qingsheng; Huang, Chong |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Lei,Liu, Qingsheng,Huang, Chong,et al. Comparing Pixel-Based Random Forest and the Object-Based Support Vector Machine Approaches to Map the Quasi-Circular Vegetation Patches Using Individual Seasonal Fused GF-1 Imagery[J]. IEEE ACCESS,2020,8:228955-228966. |
| APA | Shi, Lei,Liu, Qingsheng,Huang, Chong,Li, He,&Liu, Gaohuan.(2020).Comparing Pixel-Based Random Forest and the Object-Based Support Vector Machine Approaches to Map the Quasi-Circular Vegetation Patches Using Individual Seasonal Fused GF-1 Imagery.IEEE ACCESS,8,228955-228966. |
| MLA | Shi, Lei,et al."Comparing Pixel-Based Random Forest and the Object-Based Support Vector Machine Approaches to Map the Quasi-Circular Vegetation Patches Using Individual Seasonal Fused GF-1 Imagery".IEEE ACCESS 8(2020):228955-228966. |
入库方式： OAI收割
来源：地理科学与资源研究所
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