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arXiv · 1906.07563

Study on the spectral reconstruction of typical surface types based on spectral library and principal component analysis

Weizhen Hou·Yilan Mao·Chi Xu·Zhengqiang Li·Donghui Li·Yan Ma·Hua Xu
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Abstract

To meet the demanding of spectral reconstruction in the visible and near-infrared wavelength, the spectral reconstruction method for typical surface types is discussed based on the USGS /ASTER spectral library and principal component analysis (PCA). A new spectral reconstructed model is proposed by the information of several typical bands instead of all of the wavelength bands, and a linear combination spectral reconstruction model is also discussed. By selecting 4 typical spectral datasets including green vegetation, bare soil, rangeland and concrete in the spectral range of 400-900 nm, the PCA results show that 6 principal components could characterized the spectral dataset, and the relative reconstructed errors are smaller than 2%. If only 6-7 selected typical bands are employed to spectral reconstruction for all the surface reflectance in 400-900 nm, except that the reconstructed error of green vegetation is about 3.3%, the relative errors of other 3 datasets are all smaller than 1.6%. The correlation coefficients of those 4 datasets are all larger than 0.99, which can effectively satisfy the needs of spectral reconstruction. In addition, based on the spectral library and the linear combination model of 4 common used bands of satellite remote sensing such as 490, 555, 670 and 865 nm, the reconstructed errors are smaller than 8.5% in high reflectance region and smaller than 1.5% in low reflectance region respectively, which basically meet the needs of spectral reconstruction. This study can provide a reference value for the surface reflectance processing and spectral reconstruction in satellite remote sensing research.

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Weizhen Hou, Yilan Mao, Chi Xu, Zhengqiang Li, Donghui Li, Yan Ma, Hua Xu. 2019-06-15. Study on the spectral reconstruction of typical surface types based on spectral library and principal component analysis. https://doi.org/10.1117/12.2521743

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