Spectral and temporal linear mixing model for vegetation classification
Spectral and temporal linear mixing model for vegetation classification
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DOI：
10.1080/01431160410001680437
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发表时间：
2004-10
影响因子：
3.4
通讯作者：
R. Tateishi;Y. Shimazaki;P. Gunin
中科院分区：
文献类型：
--
作者：
R. Tateishi;Y. Shimazaki;P. Gunin
本研究的目的是寻找一种更好的植被亚像元分类方法。线性混合模型（LMM）的提出的新技术是光谱LMM和时间LMM的顺序组合。“相对绿色植被”的亚像元分量由光谱LMM得到，植被类型的亚像元分量由随后的时间LMM估计。所提出的方法被施加到五个时间Landsat增强专题制图仪（ETM）图像为2000年贝加尔湖，俄罗斯南部地区。主要植被类型有松树、桦树/白杨、灌木和小麦，杂草丛生。植被类型的地面实况数据是通过实地调查和专家对Landsat ETM图像的目视解译而获得的。将该方法与传统LMM方法的分类结果进行了比较，并对它们进行了仿真，结果表明，该方法具有更好的分类精度。
The objective of this study is to find a better method for sub-pixel classification of vegetation. The proposed new technique of a linear mixing model (LMM) is the sequential combination of spectral LMM and temporal LMM. Sub-pixel components of ‘relative green vegetation’ are derived by spectral LMM; sub-pixel components of vegetation types are estimated by subsequent temporal LMM. The proposed method was applied to five temporal Landsat Enhanced Thematic Mapper (ETM) images for the year 2000 for areas south of Lake Baikal, Russia. Dominant vegetation types there are pine, birch/aspen, shrubs and wheat with weedy plants. Ground truth data of vegetation types were prepared by field survey and visual interpretation of Landsat ETM images by experts. Both the comparisons of classification results among the proposed method and conventional LMM methods and the simulation results among them indicate that the proposed spectral and temporal LMM has better accuracy than conventional methods.