| 分层分类与监督分类相结合的遥感分类法研究 |
| |
| 引用本文： | 刘礼,于强.分层分类与监督分类相结合的遥感分类法研究[J].林业调查规划,2007,32(4):37-39,44. |
| |
| 作者姓名： | 刘礼 于强 |
| |
| 作者单位： | 1. 南京林业大学森林资源与环境学院,江苏,南京,210037
2. 昆明理工大学国土资源工程学院,云南,昆明,650000 |
| |
| 基金项目： | 吉林省科技厅科研项目，中国科学院遥感应用研究所遥感信息科学重点实验室基金 |
| |
| 摘 要： | 遥感分类技术是获取土地利用/覆盖数据的主要方法.分层分类思想强调将分类过程逐级进行,每层选用不同的分类标准和方法;监督分类是基于传统统计分析的分类法,具有算法成熟,简便易行的特点.将2种方法相结合,建立起一个复合分类模型,并在SPOT影像上进行试验.试验证明:该方法能有效地提高分类精度,比单一使用监督分类法得到的结果精度提高了8.41%.
|
| 关 键 词： | 分层分类 监督分类 遥感分类 决策树分类器 复合分类模型 遥感影像 |
| 文章编号： | 1671-3168（2007）04-0037-03 |
| 修稿时间： | 2007-05-11 |
A Study on A Classification Method of Remote Sensing Combined Stratified Classification with Supervised Classification |
| |
| LIU Li,YU Qiang.A Study on A Classification Method of Remote Sensing Combined Stratified Classification with Supervised Classification[J].Forest Inventory and Planning,2007,32(4):37-39,44. |
| |
| Authors: | LIU Li YU Qiang |
| |
| Institution: | 1. College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037 Jiangsu; 2. Faculty of Land Resources and Engineering, Kunming University of Sciences and Technology, Kunming 650000 Yunnan, China |
| |
| Abstract: | The technology of remote sensing image classification is the main means of LUCC research. The stratified classification is a method based on the idea of division of layers step by step and different criteria and methods in each layer; while the supervised classification highlights traditional statistical and analytic approach, which is a mature algorithm, simple and convenient as well as easily being operated. Combining these two methods is to establish a compound classification model which is tested in SPOT image. The result shows that this method can effectively improve precision of classification. In detail, compared with supervised classification method, the result from compound method improves 8.41% of the precision. |
| |
| Keywords: | stratified classification supervised classification remote sensing classification classification frame of decision tree compound model remote sensing image |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
