基于水稻高光谱遥感数据的PLS波长选择研究
PLS Wavelength Selection by Hyperspectral Remote Sensing in Rice
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摘要: 对武汉地区不同生长状况下的水稻冠层进行了遥感监测,采用偏最小二乘(PLS)分析方法分别对水稻叶面氮含量、叶绿素含量进行了波长选择研究,并通过构建8种波长组合进行PLS水稻叶面氮含量反演分析和比较,选择出最合适的反演波长。多种波长(波段)组合进行叶面氮含量反演的验证表明,采用552 nm、675 nm7、52 nm7、76 nm的4波长组合是最适合叶面氮含量反演的波长选择结果,同时,采用光谱能量空间变换的形式能较好地改善波长选择的反演效果。Abstract: The objective of the present research was to identify and select the spectral wavelength with the partial least square(PLS) method for the study of rice.We use PLS method to select the wavelengths which have the largest weightings for rice leaf biochemical concentrations respectively for leaf nitrogen concentration and chloroghyll-a concentration.In order to evaluate the wavelength selection results,we construct eight kinds of wavelength(bands) combinations for rice leaf nitrogen concentration inversion analysis by PLS.The most appropriate wavelengths are 552 nm,675 nm,752 nm,776 nm,respectively.
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