Hyperspectral imaging (HSI) is an emerging technology that integrates conventional
imaging and spectroscopy to attain both spatial and spectral information
from an object. The spectral images, collected in the spectral cube, are tens
of hundreds and the information we receive from them is crucial for many applications,
such as bio-medical technology, remote sensing, microscopy etch.
Nevertheless, the current state of the art includes HSI systems which need
long acquisition time, something that prevents them from observing any dynamically
developing phenomena. Also, they are expensive and sizeable, which
makes them inaccessible to many important applications. To address these
limitations, a theoretical real-time snapshot spectral imaging system (SNSI)
that captures a small number of spectral bands, and by using dimensionality
expansion techniques, provides real time HSI, is investigated in this study. A
comparative study of the state of the art was conducted under the assumption
of various hardware architectures (RGB narrow/wide, three, six, nine, and
twelve spectral channels). Furthermore, two new algorithms called K-Fourier
and 2Level are proposed and compared in terms of minimizing the estimation
error of the uncaptured spectral images. The novelty of the proposed methods
stems mainly from the reduction of the dimensionality of the space needed to be
reconstructed. Experiments on standard color charts show that the K-Fourier
and non-linear kernels outperform the other competing methods. Looking at the
same problem from another perspective, the most feature-rich training yields to
higher estimation accuracy. On that account, a great amount of band selection
techniques, which are based on similarity-measurement, dynamic programming,
and evolutionary formulas were analyzed and compared. Genetic Algorithms
turned out to be the most promising feature selection technique, since it dramatically
improves the space reconstruction error. Moreover, we introduce a
nonlinear transformation of reflectance values to ensure that the estimated reflection
spectra fulfill physically motivated boundary conditions. Ending up,
these findings set the basis for the development of a powerful SNSI system.
Medical diagnosis is expected to be a leading application of this novel approach.