Authors:
Yuji Sakatoku
1
;
Jay Arre Toque
2
and
Ari Ide-Ektessabi
1
Affiliations:
1
Graduate School of Engineering, Kyoto University, Japan
;
2
Kyoto university, Japan
Keyword(s):
Multispectral imaging, Hyperspectral image, Spectral reflectance, Regression analysis, AIC, Cultural heritage.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Coding and Compression
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Physics Imaging (Radar Imaging, Photoelectronics, Molecular Imaging)
Abstract:
The purpose of this study is to develop an efficient appraoch for producing hyperspectral images by using reconstructed spectral reflectance from multispectral images. In this study, an indirect reconstruction based on regression analysis was employed because of its stability to noise and its practicality. In this approach however, the regression model selection and channel selection when acquiring the multispectral images play important roles, which consequently affects the efficiency and accuracy of reconstruction. The optimum regression model and channel selection were investigated using the Akaike information criterion (AIC). By comparing the model based on the AIC model based on the pseudoinverse method (the pseudinverse method is a widely used reconstruction technique), RMSE could be reduced by fifty percent. In addition, it was shown that AIC-based model has good stability to noise.