Land-cover classification of the Yellow River Delta wetland based on multiple end-member spectral mixture analysis and a Random Forest classifier
文献类型：期刊论文
| 作者 | Liu, Jiantao1; Feng, Quanlong1; Gong, Jianhua1; Zhou, Jieping1; Li, Yi1 |
| 刊名 | International Journal of Remote Sensing
 |
| 出版日期 | 2016 |
| 卷号 | 37期号:8页码:1845-1867 |
| 关键词 | ANTARCTIC ICE-STREAM TIBETAN PLATEAU LINE VELOCITY PENETRATION ALTIMETRY TRACKING MOTION SHEET SRTM |
| 通讯作者 | Gong, Jianhua (gongjh@radi.ac.cn) |
| 英文摘要 | ABSTRACT: As an important ecosystem, wetlands play a crucial role in both regional and global environments. Accurate land-cover classification can facilitate the management and understanding of wetlands. Considering the timely and cost-effective characteristics of remote sensing, this technique was used to obtain land-cover information for the Yellow River Delta (YRD) wetland in this investigation. Landsat-8 Operational Land Imager (OLI) sensor data were selected for the data set in this study. A combined approach of multiple end-member spectral mixture analysis (MESMA) and Random Forest (RF) was developed for land-cover classification mapping of the YRD wetland. This study aimed (1) to determine whether the MESMA technique in combination with RF significantly improves the accuracy of classification in complex landscapes such as the YRD wetland, (2) to determine whether the RF classifier shows good performance in land-cover classification of the YRD wetland, and (3) to compare the proposed method with the traditional Maximum Likelihood Classifier (MLC). The proposed hybrid method showed good performance, with an overall accuracy of 89.5% and a kappa coefficient (κ) of 0.88. The inclusion of fractional information derived from MESMA can improve the classification accuracy by 2–3%. In addition, through a comparison with traditional maximum likelihood (ML) methodology, the effectiveness of the proposed approach was evaluated. Overall, the proposed approach in this study can relatively accurately delineate a land-cover classification map of the YRD wetland with Landsat-8 OLI remotely sensed data. © 2016 Informa UK Limited, trading as Taylor & Francis Group. |
| 学科主题 | Remote Sensing; Imaging Science & Photographic Technology |
| 类目[WOS] | Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20161702285273 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39325]  |
| 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. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2. Zhejiang-CAS Application Centre for Geoinformatics, Jiaxing, China |
| 推荐引用方式 GB/T 7714 | Liu, Jiantao,Feng, Quanlong,Gong, Jianhua,et al. Land-cover classification of the Yellow River Delta wetland based on multiple end-member spectral mixture analysis and a Random Forest classifier[J]. International Journal of Remote Sensing,2016,37(8):1845-1867. |
| APA | Liu, Jiantao,Feng, Quanlong,Gong, Jianhua,Zhou, Jieping,&Li, Yi.(2016).Land-cover classification of the Yellow River Delta wetland based on multiple end-member spectral mixture analysis and a Random Forest classifier.International Journal of Remote Sensing,37(8),1845-1867. |
| MLA | Liu, Jiantao,et al."Land-cover classification of the Yellow River Delta wetland based on multiple end-member spectral mixture analysis and a Random Forest classifier".International Journal of Remote Sensing 37.8(2016):1845-1867. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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