Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture
文献类型：期刊论文
| 作者 | Tian-Xiang Zhang; Jin-Ya Su; Cun-Jia Liu; Wen-Hua Chen |
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2019 |
| 卷号 | 16期号:1页码:16-26 |
| 关键词 | Sentinel-2A remote sensing image classification supervised learning precision agriculture. |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-018-1143-x |
| 英文摘要 | Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices, such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress (e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes (crop, tree, soil, water and road) with the support vector machines (SVMs) algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands (red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information (MI), and full bands of on-board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42318]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK |
| 推荐引用方式 GB/T 7714 | Tian-Xiang Zhang,Jin-Ya Su,Cun-Jia Liu,et al. Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture[J]. International Journal of Automation and Computing,2019,16(1):16-26. |
| APA | Tian-Xiang Zhang,Jin-Ya Su,Cun-Jia Liu,&Wen-Hua Chen.(2019).Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture.International Journal of Automation and Computing,16(1),16-26. |
| MLA | Tian-Xiang Zhang,et al."Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture".International Journal of Automation and Computing 16.1(2019):16-26. |
入库方式： OAI收割
来源：自动化研究所
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