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遥感技术与应用  2000, Vol. 15 Issue (1): 55-59    DOI: 10.11873/j.issn.1004-0323.2000.1.55
遥感应用     
应用光谱混合分析法从SPOT影像提取槟榔树专题信息
许 珺,李 策,黄 绚
(中国科学院地理所资源与环境信息系统国家重点实验室 北京  100101)
Application of Linear Spectral Mixture Analysis in Extracting Areca Thematic Information from SPOT Image
XU Jun,LI Ce,HUANG Xuan
(LREIS,Institute of Geography,The Chinese Academy of Sciences,Beijing100101)
 全文: PDF 
摘要:

台湾地区槟榔树分布面积较大,但是种植密度不高,用通常的遥感影像分类和专题信息提取方法不能有效地提取槟榔树的分布范围。光谱混合分析是将一个像元看成多种地物的组合,分别求出各种地物所占的比例。运用线性光谱混合分析的方法,对台湾的SPOT卫星影像进行像元分解,提出了如何利用低光谱分辨率影像分解像元的方法。通过与最大似然分类和阈值法提取结果的比较,表明光谱混合分析法不仅有效地提取了槟榔树分布的范围,还计算出槟榔树分布的密度,是一种较好的方法。

关键词: 像元分解线性光谱混合槟榔树    
Abstract:

There are many areca trees planted in Taiwan for economic reason. But the areca will do great harm to environment and people' s health, and it will lead to slope erosion for its shallow root, so the planting of areca must be controlled. Remote sensing provides a useful means to monitoring the planted area of areca. But the planted densities of areca are usually low, it is difficult to identify the arecas by ordinary methods even in high ground resolution remote sensing images such as SPOT. Linear spectral mixture analysis is a method to look a pixel as composed of many different kinds of endnumbers, and it can calculate the proportion of each kind of endnumber in each pixel, so it is useful to identify the small objects in the images. In this paper, linear spectral mixture analysis is used to calculate the density of areca. Some countermeasures were put forward to make up the shortcoming of less spectral resolution of SPOT images.Then the result of linear spectral mixture analysis was compared with those of maximum likeness classification and extraction by threshold. It can be found that the result from linear spectral mixture analysis can not only extract the correct area of areca, but also calculate its density, and it is much better than those of other two methods.

Key words: Linear spectral mixture analysis    Areca trees    SPOT image
收稿日期: 1999-07-16 出版日期: 2012-02-22
:  TP 75  
作者简介: 许珺(1972-)女,博士生,从事遥感地学分析及地理信息系统应用的研究。
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引用本文:

许 珺,李 策,黄 绚. 应用光谱混合分析法从SPOT影像提取槟榔树专题信息[J]. 遥感技术与应用, 2000, 15(1): 55-59.

XU Jun,LI Ce,HUANG Xuan. Application of Linear Spectral Mixture Analysis in Extracting Areca Thematic Information from SPOT Image. Remote Sensing Technology and Application, 2000, 15(1): 55-59.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2000.1.55        http://www.rsta.ac.cn/CN/Y2000/V15/I1/55


〔1〕 Bastin L.Comparision of Fuzzy C-means Classification,Linear Mixture Modeling and MLC Probabilities as Tools for Unmixing Coarse Pixels〔J〕.INT J Remote Sensing,1997,(18):3629~3648.
〔2〕 Bajjouk T,Populus J,Guillaumont B.Quantification of Subpixel Cover Fractions Using Principal Component Analysis and a Linear Programming Method: Application to the Coastal Zone of Roscoff (France)〔J〕.Remote Sensing of Environment, 1998,(64):153~165.
〔3〕 Gross HN,Chott J R.Application of Spectral Mixture Analysis and Image Fusion Techniques for Image Sharpening〔J〕.Remote Sensing of Environment, 1998(63):85~94.
〔4〕 陈述彭,童庆禧,郭华东.遥感信息机理研究〔M〕.北京:科学出版社,1998.
〔5〕 刘政凯,岑曙炜.成像光谱遥感图像的有限光谱混合分析〔J〕.环境遥感,1996,11(1):32~37.
〔6〕 Cross A M,Settle J S,Drake N A,et al.Subpixel Measurement of Tropical Forest Cover Using AVHRR Data〔J〕. INT J Remote Sensing, 1991, (12): 1119~1129.
〔7〕 Settle J J,Drake N A. Linear mixing and the Estimation of Ground Cover Proportions〔J〕.INT J Remote Sensing,1993,(14):1159~1177.
〔8〕 Mertes L A K, Smith M O,Adams J B.Estimating Suspended Sediment Concentrations in Surface Water of the Amazon River Wetlands from Landsat Images〔J〕.Remote Sensing of Environment,1993,43:281~301.
〔9〕 杨凯,卢健,林开愚,等.遥感图像处理原理与方法〔M〕.北京:测绘出版社,1988.
〔10〕Bateson A, Curtiss B.A Method for Manual Endnumber Selection and Spectral Umixing〔J〕.Remote Sensing of Environment,1996,55:229~243.
〔11〕林敏基.海洋与海岸带遥感应用〔M〕.北京:海洋出版社,1991.
〔12〕杨存建,徐美,黄朝永.遥感信息机理的水体提取方法的探讨〔J〕.地理研究,1996,增刊:86~90.

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