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- W2015296073 abstract "Low-altitude hyperspectral observation systems are promising sensing tools for acquisition of optical remote-sensing data under the humid subtropical climate in Japan. The system is also capable of acquiring leaf-scale optical information free from atmospheric effect. However, the leaf-scale hyperspectral data are affected by shading and various illumination conditions such that it is difficult to obtain consistent characteristics of the spectral information. The aim of this article is the extraction of Lambert coefficients as an inherent leaf spectral profile. In this work, we propose a dichromatic model-based principal component analysis on hyperspectral data by utilizing leaf-scale hyperspectral data in order to diminish the spectral difference caused by the illumination condition and bidirectional reflectance distribution function. The results show that indices of chlorophyll content based on the estimated Lambert coefficients are consistent with the growth stages of a paddy field, whether the illumination condition is clear sky or overcast." @default.
- W2015296073 created "2016-06-24" @default.
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- W2015296073 date "2012-10-08" @default.
- W2015296073 modified "2023-09-27" @default.
- W2015296073 title "Estimation of Lambert parameter based on leaf-scale hyperspectral images using dichromatic model-based PCA" @default.
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- W2015296073 doi "https://doi.org/10.1080/01431161.2012.720047" @default.
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