A Comparative Evaluation of Spectral Reflectance Representations for Spectrum Reconstruction, Interpolation and Classification
Abstract
Due to the high dimensionality of spectral data, spec-trum representation techniques have often concentrated on modelling the spectra as a linear combination of a small ba-sis set. Here, we focus on the evaluation of a B-Spline rep-resentation, a Gaussian mixture model, PCA and wavelets when applied to represent real-world spectrometer and spectral image data. These representations are important since they open up the possibility of reducing densely sam-pled spectra to a compact form for spectrum reconstruction, interpolation and classification. In particular, we shall per-form an evaluation of these representations for the above tasks on two datasets consisting of reflectance spectra and hyperspectral images. 1.
Cite
Text
Huynh and Robles-Kelly. "A Comparative Evaluation of Spectral Reflectance Representations for Spectrum Reconstruction, Interpolation and Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2013. doi:10.1109/CVPRW.2013.56Markdown
[Huynh and Robles-Kelly. "A Comparative Evaluation of Spectral Reflectance Representations for Spectrum Reconstruction, Interpolation and Classification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2013.](https://mlanthology.org/cvprw/2013/huynh2013cvprw-comparative/) doi:10.1109/CVPRW.2013.56BibTeX
@inproceedings{huynh2013cvprw-comparative,
title = {{A Comparative Evaluation of Spectral Reflectance Representations for Spectrum Reconstruction, Interpolation and Classification}},
author = {Huynh, Cong Phuoc and Robles-Kelly, Antonio},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2013},
pages = {328-335},
doi = {10.1109/CVPRW.2013.56},
url = {https://mlanthology.org/cvprw/2013/huynh2013cvprw-comparative/}
}