STUDY ON FOREST VEGETATION CLASSIFICATION BASED ON MULTI-TEMPORAL REMOTE SENSING IMAGES

被引：0
|
作者：
Jing, Xia [2 ]
Wang, JiHua [1 ]
Huang, WenJiang [1 ]
Liu, LiangYun [1 ]
Wang, JinDi [2 ]
机构：
[1] Natl Engn Res Ctr Informat Technol Agr, POB 2449-26, Beijing 100097, Peoples R China
[2] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China
来源：
COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE II, VOL 1 | 2009年 / 293卷
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
multi-temporal; remote sensing; forest vegetation; classification;
D O I：
暂无
中图分类号：
S2 [农业工程];
学科分类号：
0828 ;
摘要：
It is very difficult to classify forest vegetation in mountain areas because of the impact of complex terrain. A new method, classification of forest vegetation based on multi-temporal remote sensing, is proposed in this paper. The forest vegetation could get better classification precision by avoiding the interactions of different plants with multi-temporal images. So it enhanced the separability of coniferous forest and broad leaf forest. The classification result showed that the accuracy Could be greatly improved by using multi-temporal remote sensing images. The overall accuracy and kappa coefficient were 81.3% and 0.72, respectively. So the method delivered in this essay has obviously technological advantages and important application potentiality in forest vegetation classification.
引用
收藏
页码：115 / +
页数：2
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