Estimating visible through near-infrared spectral reflectance from a sensor radiance spectrum

Developing an optimum computer-designed multispectral system comprising a monochrome CCD camera and a liquid-crystal tunable filter

In a previous work [J. Opt. Soc. Am. A24, 942 (2007)JOAOD60740-323210.1364/JOSAA.24.000942] we made a complete theoretical and computational study of the influence of several parameters on the behavior of a planned multispectral system for imaging skylight, including the number of sensors and the spectral estimation algorithm. Here we follow up this study by using all the information obtained in the computational simulations to implement a real multispectral imaging system based on a monochrome CCD camera and a liquid-crystal tunable filter (LCTF). We were able to construct the optimum Gaussian sensors found in the simulations by adjusting the exposure times of some of the transmittance modes of the LCTF, hence obtaining really accurate spectral estimations of skylight with only a few optimum sensors.

Reflectance recovery for airborne sensor images of 3D scenes

An airborne sensor measures the radiance spectrum, which is dependent on the spectral reflectance of the ground material, the orientation of the material surface, and the atmospheric and illumination conditions. We present an algorithm to estimate the surface spectral reflectance, given the sensor radiance spectrum corresponding to a single pixel. The algorithm uses a nonlinear physics-based image formation model. A low-dimensional linear subspace model is used for the reflectance spectra. The solar radiance, sky radiance, and path-scattered radiance are dependent on the environmental conditions and viewing geometry, and this interdependence is considered by using a coupled-subspace model for these spectra. The algorithm uses the Levenberg-Marquardt method to estimate the subspace model parameters. We have applied the algorithm to a large set of synthetic and real data.

Analytic solution for separating spectra into illumination and surface reflectance components

The measured light spectrum is the result of an illuminant interacting with a surface. The illuminant spectral power distribution multiplies the surface spectral reflectance function to form a color signal--the light spectrum that gives rise to our perception. Disambiguation of the two factors, illuminant and surface, is difficult without prior knowledge. Previously [IEEE Trans. Pattern Anal. Mach. Intell.12, 966 (1990); J. Opt. Soc. Am. A21, 1825 (2004)], one approach to this problem applied a finite-dimensional basis function model to recover the separate illuminant and surface reflectance components that make up the color signal, using principal component bases for lights and for reflectances. We introduce the idea of making use of finite-dimensional models of logarithms of spectra for this problem. Recognizing that multiplications turn into additions in such a formulation, we can replace the original iterative method with a direct, analytic algorithm with no iteration, resulting in a speedup of several orders of magnitude. Moreover, in the new, logarithm-based approach, it is straightforward to further design new basis functions, for both illuminant and reflectance simultaneously, such that the initial basis function coefficients derived from the input color signal are optimally mapped onto separate coefficients that produce spectra that more closely approximate the illuminant and the surface reflectance for any given dimensionality. This is accomplished by using an extra bias correction step that maps the analytically determined basis function coefficients onto the optimal coefficient set, separately for lights and surfaces, for the training set. The analytic equation plus the bias correction is then used for unknown input color signals.