In polarimetric imaging systems, the main source of perturbations may not be detection noise but fluctuations of the Mueller matrices in the scene. In this case, we propose a method for determining the illumination and analysis polarization states that allow reaching the highest target detection performance. We show with simulations and real-world images that, in practical applications, the statistics of Mueller matrix fluctuations have to be taken into account to optimize polarimetric imagery. (C) 2011 Optical Society of America
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[Show abstract][Hide abstract] ABSTRACT: In active scalar polarimetric imaging systems, the illumination and analysis polarization states are degrees of freedom that can be used to maximize the performance. These optimal states depend on the statistics of the noise that perturbs image acquisition. We investigate the problem of optimization of discrimination ability (contrast) of such imagers in the presence of three different types of noise statistics frequently encountered in optical images (Gaussian, Poisson, and Gamma). To compare these different situations within a common theoretical framework, we use the Bhattacharyya distance and the Fisher ratio as measures of contrast. We show that the optimal states depend on a trade-off between the target/background intensity difference and the average intensity in the acquired image, and that this trade-off depends on the noise statistics. On a few examples, we show that the gain in contrast obtained by implementing the states adapted to the noise statistics actually present in the image can be significant.
[Show abstract][Hide abstract] ABSTRACT: In active polarization imaging, one frequently needs to be insensitive to noninformative spatial intensity fluctuations. We investigate a way of solving this issue with general state contrast (GSC) imaging. It consists in acquiring two scalar polarimetric images with optimized illumination and analysis polarization states, then forming a ratio. We propose a method for maximizing the discrimination ability between a target and a background in GSC images by determining the optimal illumination and analysis states. A further advantage of this approach is to provide an objective way of quantifying the performance improvement obtained by increasing the number of degrees of freedom of a GSC imager. The efficiency of this approach is demonstrated on simulated and real-world images.
Preview · Article · Jun 2012 · Journal of the Optical Society of America A
[Show abstract][Hide abstract] ABSTRACT: Active (Mueller matrix) remote sensing is an under-utilized technique for material discrimination and classication. A full Mueller matrix instrument returns more information than a passive (Stokes) polarimeter; Mueller polarimeters measure depolarization and other linear transformations that materials impart on incident Stokes vectors, which passive polarimeters cannot measure. This increase in information therefore allows for better classication of materials (in general). Ideally, material classication over the entire polarized BRDF is desired, but sets of Mueller matrices for dierent materials are generally not separable by a linear classier over elevation and azimuthal target angles. We apply non-linear support vector machines (SVM) to classify materials over BRDF (all relevant angles) and show variations in receiver operator characteristic curves with scene composition and number of Mueller matrix channels in the observation.