| 基于GOODALL相近指数的遥感图像和其它空间数据综合分类方法 |
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| 引用本文： | 田 青 Enrico Feoli.基于GOODALL相近指数的遥感图像和其它空间数据综合分类方法[J].遥感学报,1999,3(3):2-192,T001. |
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| 作者姓名： | 田 青 Enrico Feoli |
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| 作者单位： | 中国科学院遥感应用研究所!北京100101(田青)，DepartmentofBiologyTriesteUniversityItaly(EnricoFeoli) |
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| 基金项目： | I C S U N I D O （ International Center for Science and High Technolog |
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| 摘 要： | 介绍 Ｄａｖｉｄ Ｗ． Ｇｏｏｄａｌｌ 的基于概率的相近指数理论，研究它被应用在遥感图像和其它空间数据综合分类中的可能性，并首次在 Ｇ Ｒ Ａ Ｓ Ｓ环境下实现了基于 Ｄａｖｉｄ Ｗ． Ｇｏｏｄａｌｌ 的相近指数的遥感图像和其它空间数据综合分类算法，并对该算法进行了测试，将分类结果与其它几种较流行的分类方法结果进行了比较。
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| 关 键 词： | 遥感图像 空间数据 综合分类算法 相近指数 |
| 修稿时间： | 1998-04-02 |
An Algorithm for Spatial Data Integrated Classification Based on GOODALL's Affinity Index |
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| TIAN QingEnrico Feoli.An Algorithm for Spatial Data Integrated Classification Based on GOODALL's Affinity Index[J].Journal of Remote Sensing,1999,3(3):2-192,T001. |
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| Authors: | TIAN QingEnrico Feoli |
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| Institution: | TIAN Qing;
(Institute of Remote Sensing Applications, Chinese Academy of Sciences 100101);
Enrico Feoli;
(Biology Department, Trieste University, Italy) |
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| Abstract: | Today many methods have been used in classifying remote sensing images. However, developing classification algorithm which is capable of processing both images and other ancillary spatial data still remains to be an active research area. In this paper, the affinity index of David W. Goodall based on probability was explained, and its application possibility in remote sensing and other spatial data integrated classification was studied. Based on Goodall's affinity index, a computer program for classifying both remote sensing and other spatial data was developed within GRASS environment. To see the result of this program, it was tested in a case study area and compared with other popular classification methods such as maximum likelihood classification and evidential classification. Through this study, we would like to know how the other spatial data can help improve the remote sensing image classification and whether the algorithm based on Goodall's affinity index is good in classifying remote sensing images and other ancillary spatial data in an integrate way. |
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| Keywords: | Remote sensing image Spatial data Multisource data classification Affinity index |
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