Rough set-derived measures in image classification accuracy assessment
文献类型：SCI/SSCI论文
| 作者 | Ge Y. |
| 发表日期 | 2009 |
| 关键词 | incomplete information-systems acquisition uncertainty agreement map |
| 英文摘要 | Currently, there are two types of measure in image classification accuracy assessment: pixel-level measures and category-level/map-level measures. These have their own limitations for representing the uncertainty at pixel and category/map levels. In addition, some of these measures derived from the error matrix are obtained by collecting reference data and then they may be affected by factors related to the sampling. This paper uses rough set theory to obtain the rough degree, rough entropy, quality of approximation and accuracy of approximation. Incorporating traditional measures, they compose one kind of three-level architecture for the classified image, which contains pixel-level measures, object/category-level measures and map-level measures. Unlike some conventional measures, these new measures can be derived directly from the supervised classification result without collecting reference data. A case study on the Landsat TM image is used to substantiate the conceptual arguments. The results demonstrate that the proposed measures are valid for measuring the accuracy of classified remotely sensed imagery and can provide additional information to conventional measures. |
| 出处 | International Journal of Remote Sensing |
| 卷 | 30 |
| 期 | 20 |
| 页 | 5323-5344 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 0143-1161 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/23954]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Ge Y.. Rough set-derived measures in image classification accuracy assessment. 2009. |
入库方式： OAI收割
来源：地理科学与资源研究所
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