Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA
文献类型：期刊论文
| 作者 | Hao, Pengyu1; Zhan, Yulin1; Wang, Li1; Niu, Zheng1; Shakir, Muhammad1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2015 |
| 卷号 | 7期号:5页码:1-13 |
| 通讯作者 | Zhan, YL (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China. |
| 英文摘要 | Currently, accurate information on crop area coverage is vital for food security and industry, and there is strong demand for timely crop mapping. In this study, we used MODIS time series data to investigate the effect of the time series length on crop mapping. Eight time series with different lengths (ranging from one month to eight months) were tested. For each time series, we first used the Random Forest (RF) algorithm to calculate the importance score for all features (including multi-spectral data, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and phenological metrics). Subsequently, an extension of the Jeffries-Matusita (JM) distance was used to measure class separability for each time series. Finally, the RF algorithm was used to classify crop types, and the classification accuracy and certainty were used to analyze the influence of the time series length and the number of features on classification performance; the features were added one by one based on their importance scores. Results indicated that when the time series was longer than five months, the top ten features remained stable. These features were mainly in July and August. In addition, the NDVI features contributed the majority of the most significant features for crop mapping. The NDWI and data from multi-spectral bands also contributed to improving crop mapping. On the other hand, separability, classification accuracy, and certainty increased with the number of features used and the time series length, although these values quickly reached saturation. Five months was the optimal time series length, as longer time series provided no further improvement in the classification performance. This result shows that relatively short time series have the potential to identify crops accurately, which allows for early crop mapping over large areas. |
| 研究领域[WOS] | Remote Sensing |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000357596200018 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38207]  |
| 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.[Hao, Pengyu 2.Zhan, Yulin 3.Wang, Li 4.Niu, Zheng 5.Shakir, Muhammad] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 6.[Hao, Pengyu] Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hao, Pengyu,Zhan, Yulin,Wang, Li,et al. Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA[J]. REMOTE SENSING,2015,7(5):1-13. |
| APA | Hao, Pengyu,Zhan, Yulin,Wang, Li,Niu, Zheng,&Shakir, Muhammad.(2015).Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA.REMOTE SENSING,7(5),1-13. |
| MLA | Hao, Pengyu,et al."Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA".REMOTE SENSING 7.5(2015):1-13. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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