| 基于中等分辨率遥感影像的桃源县竹林信息提取研究 |
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| 引用本文： | 桂玲,孙华,陈利.基于中等分辨率遥感影像的桃源县竹林信息提取研究[J].中国农学通报,2012,28(1):85-91. |
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| 作者姓名： | 桂玲 孙华 陈利 |
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| 作者单位： | 中南林业科技大学,长沙,410004 |
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| 基金项目： | 湖南省高等学校科学研究项目“高分辨率遥感影像森林结构参数反演研究”（11C1313）. |
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| 摘 要： | 为了提高竹资源调查效率，为资源的合理开发和科学规划提供依据，以湖南省桃源县为研究对象，以中等分辨率Landsat TM遥感影像、桃源县二类调查资源分布图等为数据源，利用ENVI 4.5对Landsat TM进行图像预处理，运用非监督分类、最大似然分类、马氏距离分类、最小距离分类4种分类法对竹林信息进行提取，并对其精度进行评价。结果表明：非监督分类、最大似然分类、最小距离分类、马氏距离分类总体精度分别为60.47%、92.15%、71.70%、82.81%，Kappa系数分别为0.4263、0.8890、0.6085、0.7595。监督分类的精度比非监督分类要高，其中最大似然法分类的总体精度、用户精度、Kappa系数均比其他3种分类精度要高，在保证竹林分类精度的同时，其他植被类型的分类精度也能得到满意的结果，因此它是进行竹林信息提取的较为理想的方法。
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| 关 键 词： | 检测 检测 |
| 收稿时间： | 2011/9/18 0:00:00 |
| 修稿时间： | 2011/10/24 0:00:00 |
The Bamboo Information Extraction Research in Taoyuan County Based on Medium Resolution Remote Sensing Images |
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| Gui Ling
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Sun Hua
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Chen Li.The Bamboo Information Extraction Research in Taoyuan County Based on Medium Resolution Remote Sensing Images[J].Chinese Agricultural Science Bulletin,2012,28(1):85-91. |
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| Authors: | Gui Ling Sun Hua Chen Li |
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| Institution: | (Central South University of Forestry＆ Technology, Changsha 410004) |
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| Abstract: | In order to improve the efficiency of bamboo resource investigation, and supply the reference for reasonably developing the resource and planning scientifically, the author took the Taoyuan County of Hunan Province as the research object with medium resolution Landsat TM remote sensing image and 2 class survey resources distribution maps of Taoyuan County for the data source. Using of ENVI 4.5 on Landsat TM by image preprocessing, using unsupervised classification, maximum likelihood classification, Mahalanobis distance classification, minimum distance classification 4 classification methods of bamboo information extraction, and its. accuracy was evaluated. The results showed_ that： unsupervised classification, maximum likelihood classification, minimum distance, Mahalanobis distance overall classification accuracy were 60.47%, 92.15%, 71.70% , 82.81% , respectively, Kappa coefficients were 0.4263, 0.8890, 0.6085, 0.7595. Supervised classification accuracy was higher than the unsupervised classification, and the maximum likelihood classification overall accuracy as well as the user accuracy. Kappa coefficient was higher than other 3 kinds of classification accuracy, at the same time, other types of vegetation classification accuracy could be satisfied with the results, so the maximum likelihood classification was the ideal method of bamboo information extraction. |
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| Keywords: | remote sensing information extraction Kappa coefficient bamboo |
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