干旱区生态环境知识资源中心(Arid): 基于不同窗口纹理特征的SVM土壤盐渍化信息提取方法与精度分析研究

Arid

	基于不同窗口纹理特征的SVM土壤盐渍化信息提取方法与精度分析研究
其他题名	Extracting of soil salinization by SVM and accuracy evaluation based on texture characteristic
	张飞; 塔西甫拉提.特依拜; 丁建丽; 田源; 依力亚斯江.努尔麦麦提; 哈学萍
ISSN	1000-6060
出版年	2009
卷号	32 期号:1 页码:57-66
中文摘要	以塔里木盆地北缘绿洲渭干河-库车河三角洲绿洲为例,借助ENVI遥感软件,利用ETM+数据,探讨了该绿洲土壤盐渍化信息提取的方法。传统的遥感图像分类方法多数在解决问题上存在精度不高、分类效率较低、不确定性强的缺陷,所以,选择好的分类方法对于提取盐渍化信息是至关重要的。近年来,将SVM应用于遥感图像分类已成为新的发展趋势。文章提出了基于纹理特征的支持向量机(Support Vector Machine,SVM)的分类方法,得出以下结论:分别结合33,55,77,99,1111,1313窗口纹理特征和光谱的SVM分类精度都很高,达到93%以上。并且在验证分类精度时,发现结合光谱和9*9窗口纹理信息的SVM分类的结果更符合实际情况。所以说加入纹理特征后使得光谱信息比较接近的3类地物(重度、中度、轻度盐渍地)的区分性增大,从而使精度提高。因此,基于纹理特征的SVM分类方法更有利于遥感图像分类和盐渍化信息监测,是地物遥感信息提取的有效途径。
英文摘要	It is of great importance to obtain the earth surface property timely and effectively,which can help us to know the relationship between human and nature phenomena and to make decision.In this paper,the author takes the North Oasis of the Tarim Basin(the delta Oasis of Weigan and Kuqa Rivers) for example,in virtue of ENVI software and using ETM+ data;discussing the method of extracting of soil salinization information.Remote sensing image classification is an important and complex problem.But conventional remote sensing image classification methods are mostly based on Bayes’ subjective probability theory;it has the shortcomings of the low classification accuracy,classification efficiency and high indeterminacy,because there are many defects,a new tendency is that the SVM has been applied to remote sensing image classification.This paper reports the classification method based on support vector machines(SVM),and introduces the fundamental theory of SVM algorithm,then incorporating of spectrum and texture information.The results indicate that on the windows of 33,55,77,99,1111,1313,the precision of classification is very high,up to above 93 %,and when validating the result of classification,we have found that the result of classification by the SVM method based on spectrum and texture information on 9*9 windows is in accordance with the fact.So we can say that joining with the texture character can easily extract the soil salinization information(severely salinized soil,moderately salinized soil,slightly salinized soil),and increases its precision.Therefore,the classification method by SVM(Support Vector Machines) based on texture characteristic can be adapted to RS image classification and monitoring of soil salinization,furthermore provide a effective way for remote sensing information extraction.
中文关键词	支持向量机 ; 光谱 ; 盐渍化 ; 灰度共生矩阵 ; 纹理特征
英文关键词	support vector machines（SVM） spectral soil salinization gray co-occurrence matrix texture characteristic
语种	中文
国家	中国
收录类别	CSCD
WOS类目	REMOTE SENSING
WOS研究方向	Remote Sensing
CSCD记录号	CSCD:3486908
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/223533
作者单位	新疆大学资源与环境科学学院, 绿洲生态教育部重点实验室, 乌鲁木齐, 新疆 830046, 中国
推荐引用方式 GB/T 7714	张飞,塔西甫拉提.特依拜,丁建丽,等. 基于不同窗口纹理特征的SVM土壤盐渍化信息提取方法与精度分析研究[J]. 新疆大学,2009,32(1):57-66.
APA	张飞,塔西甫拉提.特依拜,丁建丽,田源,依力亚斯江.努尔麦麦提,&哈学萍.(2009).基于不同窗口纹理特征的SVM土壤盐渍化信息提取方法与精度分析研究.,32(1),57-66.
MLA	张飞,et al."基于不同窗口纹理特征的SVM土壤盐渍化信息提取方法与精度分析研究".32.1(2009):57-66.