Automatic recognition of loess landforms using Random Forest method
文献类型：期刊论文
| 作者 | ZHAO Wu-fan; XIONG Li-yang; DING Hu; TANG Guo-an |
| 刊名 | Journal of Mountain Science
 |
| 出版日期 | 2017-05 |
| 卷号 | 14期号:5页码:885-897 |
| 关键词 | Landform Recognition Random Forest Feature Fusion Dem Loess Landform |
| ISSN号 | 1672-6316 |
| DOI | 10.1007/s11629-016-4320-9 |
| 通讯作者 | XIONG Li-yang |
| 文献子类 | 期刊论文 |
| 英文摘要 | The automatic recognition of landforms is regarded as one of the most important procedures to classify landforms and deepen the understanding on the morphology of the earth. However, landform types are rather complex and gradual changes often occur in these landforms, thus increasing the difficulty in automatically recognizing and classifying landforms. In this study, small-scale watersheds, which are regarded as natural geomorphological elements, were extracted and selected as basic analysis and recognition units based on the data of SRTM DEM. In addition, datasets integrated with terrain derivatives (e.g., average slope gradient, and elevation range) and texture derivatives (e.g., slope gradient contrast and elevation variance) were constructed to quantify the topographical characteristics of watersheds. Finally, Random Forest (RF) method was employed to automatically select features and classify landforms based on their topographical characteristics. The proposed method was applied and validated in seven case areas in the Northern Shaanxi Loess Plateau for its complex andgradual changed landforms. Experimental results show that the highest recognition accuracy based on the selected derivations is 92.06%. During the recognition procedure, the contributions of terrain derivations were higher than that of texture derivations within selected derivative datasets. Loess terrace and loess mid-mountain obtained the highest accuracy among the seven typical loess landforms. However, the recognition precision of loess hill, loess hill–ridge, and loess sloping ridge is relatively low. The experiment also showsthat watershed-based strategy could achieve better results than object-based strategy, and the method of RF could effectively extract and recognize the feature of landforms. |
| 语种 | 英语 |
| 源URL | [http://ir.imde.ac.cn/handle/131551/18714]  |
| 专题 | Journal of Mountain Science _Journal of Mountain Science-2017_Vol14 No.5 |
| 推荐引用方式 GB/T 7714 | ZHAO Wu-fan,XIONG Li-yang,DING Hu,et al. Automatic recognition of loess landforms using Random Forest method[J]. Journal of Mountain Science,2017,14(5):885-897. |
| APA | ZHAO Wu-fan,XIONG Li-yang,DING Hu,&TANG Guo-an.(2017).Automatic recognition of loess landforms using Random Forest method.Journal of Mountain Science,14(5),885-897. |
| MLA | ZHAO Wu-fan,et al."Automatic recognition of loess landforms using Random Forest method".Journal of Mountain Science 14.5(2017):885-897. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
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