Linear Modelling for Spectral Images Based on Truncated Fourier Series
Abstract
Reflectance spectra of hyperspectral images of the natural scenes are supposed to represent the real world better than any certain classes of natural and man-made spectral reflectance. But spectral images contain a large volume of data and place considerable demands on computer hardware and software compared with standard trichromatic image storage and processing. Although principal component analysis (PCA) based low-dimensional linear models have been widely used in spectral image encoding and compression, there is no a single PCA linear model derived from one data set can be guaranteed to accurate represent other data sets. In this study, we proposed a spectral image encoding method by a single linear model constructed by truncated Fourier series, in which a limited number of parameters that is proportional to the highest frequency cut off if the low-pass hypothesis is valid for any of the data sets. In this paper, several groups of hyperspectral images have been processed using truncated Fourier series, the encoded image are analysed in terms of chromaticity and spectral root mean square (RMS) errors. Results show spectral images can be efficiently compressed when the frequency reach certain limit, and the color information can be well preserved, but there are also large variations across different data sets.
DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3275
Keywords
spectral image; truncated Fourier series; linear model
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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).