Subsurface polarimetric (differential polarization, degree of polarization or Mueller matrix) imaging of various targets in
turbid media shows image contrast enhancement compared with total intensity measurements. The image contrast
depends on the target immersion depth and on both target and background medium optical properties, such as scattering
coefficient, absorption coefficient and anisotropy. The differential polarization image contrast is usually not the same for
circularly and linearly polarized light. With linearly and circularly polarized light we acquired the orthogonal state
contrast (OSC) images of reflecting, scattering and absorbing targets. The targets were positioned at various depths
within the container filled with polystyrene particle suspension in water. We also performed numerical Monte Carlo
modelling of backscattering Mueller matrix images of the experimental set-up. Quite often the dimensions of container,
its shape and optical properties of container walls are not reported for similar experiments and numerical simulations.
However, we found, that depending on the photon transport mean free path in the scattering medium, the above
mentioned parameters, as well as multiple target design could all be sources of significant systematic errors in the
evaluation of polarimetric image contrast. Thus, proper design of experiment geometry is of prime importance in order to
remove the sources of possible artefacts in the image contrast evaluation and to make a correct choice between linear and
circular polarization of the light for better target detection.
Active imaging systems that illuminate the scene with polarized light and acquire two images in two orthogonal
polarizations yield information about the intensity contrast and the Orthogonal State Contrast (OSC) in the
scene. However, in real systems, the illumination is often spatially or temporally non uniform. We first study
the influence of this non uniformity on estimation performances. We derive the Cramer Rao Lower Bound and
determine a profile likelihood-based estimator. We demonstrate the efficiency of this estimator and compare its
performance with other standard estimators as a function of the degree of non-uniformity of the illumination.
Concerning target detection, illumination non uniformity creates artificial intensity contrasts that can lead to
false alarms. We derive the Generalized Likelihood Ratio Test (GLRT) detectors when intensity information is
taken into account or not, and determine the relevant expressions of the contrast in these two situations. These
results are used to determine in which cases taking intensity information in addition to polarimetric information
is relevant or not.
In this article we address the design and exploitation of a real field laboratory demonstrator combining active
polarimetric and multispectral modes in a single acquisition. Its buildings blocks, including a multi-wavelength
pulsed optical parametric oscillator at emission side, and a hyperspectral imager with polarimetric capability at
reception side, are described. The results obtained with this demonstrator are illustrated on some examples and