
Proceedings Paper
Kernel based simplification of canopy reflectance model using partial least square regressionFormat | Member Price | Non-Member Price |
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Paper Abstract
Inversion is an important process in remote sensing. In order to improve the stability and accuracy of inversion, in this
article, we applied kernel forms of AMBRALS (Algorithm for Model Bidirectional Reflectance Anisotropies of the Land
Surface) and PLS (Partial Least Square) regression technique to simplify a canopy reflectance model SAILH
(Scattering by Arbitrarily Inclined Leaves, with Hotspot effect). PLS is a statistical method used for regression highly
collinear variable data. Kernel-driven model is a semi-empirical model with linearity form of "kernels", and these
kernels can be explained in physics. We generated 24 typical canopy cover scenes by combining the canopy parameters
of SAILH model. For each scene, we used PLS regression to estimate the coefficients of our new model. The results
suggest the new model is acceptable in stability and accuracy. Base on the new model, we defined sensitivity matrix to
assess the correlations of directional observations data, which can help to choose appropriate directions when inversion.
Paper Details
Date Published: 8 August 2007
PDF: 11 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 675214 (8 August 2007); doi: 10.1117/12.760659
Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information
Weimin Ju; Shuhe Zhao, Editor(s)
PDF: 11 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 675214 (8 August 2007); doi: 10.1117/12.760659
Show Author Affiliations
Yingjie Yu, Beijing Normal Univ. (China)
Xihan Mu, Beijing Normal Univ. (China)
Qiang Liu, Beijing Normal Univ. (China)
Xihan Mu, Beijing Normal Univ. (China)
Qiang Liu, Beijing Normal Univ. (China)
Zhigang Liu, Beijing Normal Univ. (China)
Yuanyuan Wang, Beijing Normal Univ. (China)
Guangjian Yan, Beijing Normal Univ. (China)
Yuanyuan Wang, Beijing Normal Univ. (China)
Guangjian Yan, Beijing Normal Univ. (China)
Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information
Weimin Ju; Shuhe Zhao, Editor(s)
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