Designing a practical system for spectral imaging of skylight

Abstract

In earlier work [J. Opt. Soc. Am. A 21, 13-23 (2004)], we showed that a combination of linear models and optimum Gaussian sensors obtained by an exhaustive search can recover daylight spectra reliably from broadband sensor data. Thus our algorithm and sensors could be used to design an accurate, relatively inexpensive system for spectral imaging of daylight. Here we improve our simulation of the multispectral system by (1) considering the different kinds of noise inherent in electronic devices such as change-coupled devices (CCDs) or complementary metal-oxide semiconductors (CMOS) and (2) extending our research to a different kind of natural illumination, skylight. Because exhaustive searches are expensive computationally, here we switch to a simulated annealing algorithm to define the optimum sensors for recovering skylight spectra. The annealing algorithm requires us to minimize a single cost function, and so we develop one that calculates both the spectral and colorimetric similarity of any pair of skylight spectra. We show that the simulated annealing algorithm yields results similar to the exhaustive search but with much less computational effort. Our technique lets us study the properties of optimum sensors in the presence of noise, one side effect of which is that adding more sensors may not improve the spectral recovery.

Multispectral synthesis of daylight using a commercial digital CCD camera

“…This has been established in previous works for spectral recovery of reflectances and daylights using simulated sensors when noise effects are not included. 3,12 Nevertheless, a recent study 22 shows that this could not be the case when different kinds of noise are considered; in this case, increasing the number of sensors and͞or the number of eigenvector does not necessarily improve the accuracy of estimated spectra in the multispectral systems.…”

“…10,11 Also, the computational design of a multispectral device to estimate the SPD of daylights with a reduced number of both simulated Gaussian and commercial sensors has been proposed. 12,22 In this paper a multispectral device to synthesize SPDs of daylight-type illuminants with high spectral and colorimetric accuracy is proposed. Based on similar techniques developed for spectral image capture of art paintings, 1,6 the algorithm is based on a priori analysis of an RGB set of signals from a white surface captured by a digital CCD camera, and it increases the number of sensors by using colored filters in front of the camera lens.…”

Recovering spectral data from natural scenes with an RGB digital camera and colored filters

“…The present work has shown, however, that a combination of the direct-mapping method and a conventional RGB digital camera with a limited number of colored filters can provide acceptably accurate estimates, complementing related work on recovering illuminant spectra in natural scenes. 3,34,37,38 Of the different combinations of colored filters, best performance was obtained with three filters. In terms of the three measures, the recovered signal had a goodnessof-fit coefficient better than 99.0%, a root-mean-square error less than 5.0%, and a CIELAB color difference less than 0.27.…”

Implementation of an intelligent sensor for measurement and prediction of solar radiation and atmospheric temperature

“…The training set was based on grouping the similar spectral radiances, according to their distances measured by the colorimetric and spectral combined metric, defined by M. A. López-Álvarez et al (2005). The third method showed that it is possible to recover zenithal sky radiance spectra, with a reduced number of broadband spectral filters used in the CCD camera.…”