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Proceedings Paper

Study on the spectral reconstruction of typical surface types based on spectral library and principal component analysis
Author(s): Weizhen Hou; Yilan Mao; Chi Xu; Zhengqiang Li; Donghui Li; Yan Ma; Hua Xu
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Paper 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.

Paper Details

Date Published: 12 March 2019
PDF: 10 pages
Proc. SPIE 11023, Fifth Symposium on Novel Optoelectronic Detection Technology and Application, 110232T (12 March 2019); doi: 10.1117/12.2521743
Show Author Affiliations
Weizhen Hou, Institute of Remote Sensing and Digital Earth (China)
Yilan Mao, Beijing Institute of Spacecraft Systems Engineering (China)
Chi Xu, Beijing Institute of Spacecraft Systems Engineering (China)
Zhengqiang Li, Institute of Remote Sensing and Digital Earth (China)
Donghui Li, Institute of Remote Sensing and Digital Earth (China)
Yan Ma, Institute of Remote Sensing and Digital Earth (China)
Hua Xu, Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 11023:
Fifth Symposium on Novel Optoelectronic Detection Technology and Application
Qifeng Yu; Wei Huang; You He, Editor(s)

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