A New Accuracy Assessment Method for One-Class Remote Sensing Classification
文献类型：期刊论文
| 作者 | Li, Wenkai; Guo, Qinghua
|
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2014 |
| 卷号 | 52期号:8页码:4621-4632 |
| 关键词 | Accuracy assessment background F-measure negative one-class remote sensing classification positive |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2013.2283082 |
| 文献子类 | Article |
| 英文摘要 | In one-class remote sensing classification, users are only interested in classifying one specific land type (positive class), without considering other classes (negative class). Previous researchers have proposed different one-class classification methods without requiring negative data. An appropriate accuracy measure is usually needed to tune free parameters/threshold and to evaluate the classification result. However, traditional accuracy measures, such as the kappa coefficient and F-measure (F), require both positive and negative data, and hence, they are not applicable for positive-only data. In this paper, we investigate a new accuracy assessment method that does not require negative data. Two new statistics F-pb (proxy of F-measure based on positive-background data) and F-cpb (prevalence-calibrated proxy of F-measure based on positive-background data) can be calculated from a modified confusion matrix, where the observed negative data are replaced by background data. To investigate the effectiveness of the new method, we produced different one-class classification results using two scenes of aerial photograph, and the accuracy values were evaluated by F-pb, F-cpb, kappa coefficient, and F. The effectiveness of F-pb in model and threshold selection was investigated as well. Experimental results show that the behaviors of F-pb, F-cpb, F, and kappa coefficient are similar, and they all rank the models by accuracy similarly. In model and threshold selection, the classification accuracy values produced by maximizing F-pb and F are similar, and they are higher than those produced by setting an arbitrary rejection fraction. Therefore, we conclude that the new method is effective in model selection, threshold selection, and accuracy assessment, and it will have important applications in one-class remote sensing classification since negative data are not needed. |
| 学科主题 | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版地 | PISCATAWAY |
| 电子版国际标准刊号 | 1558-0644 |
| WOS关键词 | SUPERVISED IMAGE CLASSIFICATION ; PRESENCE-ABSENCE MODELS ; SPECIES DISTRIBUTIONS ; F-SCORE ; PREDICTION ; SUPPORT ; SET |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000332598500012 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Science Foundation of China [31270563] ; National Science Foundation [BDI-0742986] |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/27222]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China; Univ Calif, Sch Engn, Merced, CA 95343 USA |
| 推荐引用方式 GB/T 7714 | Li, Wenkai,Guo, Qinghua. A New Accuracy Assessment Method for One-Class Remote Sensing Classification[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2014,52(8):4621-4632. |
| APA | Li, Wenkai,&Guo, Qinghua.(2014).A New Accuracy Assessment Method for One-Class Remote Sensing Classification.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,52(8),4621-4632. |
| MLA | Li, Wenkai,et al."A New Accuracy Assessment Method for One-Class Remote Sensing Classification".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 52.8(2014):4621-4632. |
入库方式： OAI收割
来源：植物研究所
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