Optimal combinations of data, classifiers, and sampling methods for accurate characterizations of deforestation
文献类型：期刊论文
| 作者 | Wu, WC; Shao, GF; Shao, GF, Purdue Univ, Dept Forestry & Nat Resources, 1159 Forest Bldg, W Lafayette, IN 47907 USA |
| 刊名 | CANADIAN JOURNAL OF REMOTE SENSING
 |
| 出版日期 | 2002 |
| 卷号 | 28期号:4页码:601-609 |
| ISSN号 | 0703-8992 |
| 英文摘要 | There are increasingly more choices from a complex of data resources, classification algorithms, and methods of training sample selections. To increase the repeatability of digital classifications of remotely sensed data with consistently high accuracy, it is essential to use optimal classification options or factors. In this paper, two temporal sets of Landsat thematic mapper (TM) data, three classifiers and three approaches of training sample selections were tested for mapping deforestation. The use of these different factors can have significant effects on classification accuracy. The mixed effects of the three factors can also magnify the variations of classification accuracy. The use of bi-temporal data, a spatial-spectral classifier, and hybrid training samples results in steadily higher classification accuracy than the combination of uni-temporal data, a spectral classifier, and image training samples. For the purpose of characterizing managed forest lands, even a small increase in overall accuracy of image classification is important because it may represent a large decrease in the variations of the producer's and user's accuracy, which in turn can reduce the uncertainties of area measurements for forest coverage. |
| 学科主题 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000177561100010 |
| 公开日期 | 2011-09-23 |
| 源URL | [http://210.72.129.5/handle/321005/55471]  |
| 专题 | 沈阳应用生态研究所_沈阳应用生态研究所 |
| 通讯作者 | Shao, GF, Purdue Univ, Dept Forestry & Nat Resources, 1159 Forest Bldg, W Lafayette, IN 47907 USA |
| 推荐引用方式 GB/T 7714 | Wu, WC,Shao, GF,Shao, GF, Purdue Univ, Dept Forestry & Nat Resources, 1159 Forest Bldg, W Lafayette, IN 47907 USA. Optimal combinations of data, classifiers, and sampling methods for accurate characterizations of deforestation[J]. CANADIAN JOURNAL OF REMOTE SENSING,2002,28(4):601-609. |
| APA | Wu, WC,Shao, GF,&Shao, GF, Purdue Univ, Dept Forestry & Nat Resources, 1159 Forest Bldg, W Lafayette, IN 47907 USA.(2002).Optimal combinations of data, classifiers, and sampling methods for accurate characterizations of deforestation.CANADIAN JOURNAL OF REMOTE SENSING,28(4),601-609. |
| MLA | Wu, WC,et al."Optimal combinations of data, classifiers, and sampling methods for accurate characterizations of deforestation".CANADIAN JOURNAL OF REMOTE SENSING 28.4(2002):601-609. |
入库方式： OAI收割
来源：沈阳应用生态研究所
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