Species identification of wetland vegetation based on spectral characteristics
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摘要: 光谱特征的选择对于湿地植被的识别精度和效率有直接的影响作用。以美国舍曼( Sherman )岛水域为研究区,基于HyMap航空高光谱遥感影像数据,分析湿地植被的一阶微分光谱和光谱吸收特征,利用逐步判别分析法筛选识别精度较好的光谱特征参数参与C4.5决策树分类。结果表明:4种湿地植被的一阶导数光谱特征差异较小,吸收特征差异性相对较大;基于一阶微分光谱特征和光谱吸收特征利用C4.5决策树进行分类,可以实现湿地植被在物种水平上的识别,并达到较好的分类精度。Abstract: Certain spectral characteristics have a direct impact on accuracy and efficiency of identifying the wetland vegetation.In this paper, the authors mapped wetland vegetation with 3 m spatial resolution for HyMap image data from Sherman Island of California ’ s Sacramento -San Joaquin delta .The first -derivative spectral features and spectral absorption features of different species were analyzed by the method of stepwise discriminate analysis , and the spectral characteristic parameters with better classification accuracy were screened to identify species of wetland vegetation in C4.5 decision tree classifier .The results showed that the absorption features of four plants have larger differences than first -derivative spectral features .The results also showed that C4.5 decision tree classifier in combination with the first -derivative spectral characteristics and spectral absorption characteristics could be effective in distinguishing wetland vegetation and allowing for species -level detection .
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Key words:
- hyperspectral remote sensing /
- spectral characteristics /
- wetland vegetation /
- HyMap
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