Uncertainty is at the heart of decision-making processes in most real-world applications. Uncertainty can be broadly categorized into two types: aleatory and epistemic. Aleatory uncertainty describes the variability in the physical system where sensors provide information (hard) of a probabilistic type. Epistemic uncertainty appears when the information is incomplete or vague such as judgments or human expert appreciations in linguistic form. Linguistic information (soft) typically introduces a possibilistic type of uncertainty. This paper is concerned with the problem of classification where the available information, concerning the observed features, may be of a probabilistic nature for some features, and of a possibilistic nature for some others. In this configuration, most encountered studies transform one of the two information types into the other form, and then apply either classical Bayesian-based or possibilistic-based decision-making criteria. In this paper, a new hybrid decision-making scheme is proposed for classification when hard and soft information sources are present. A new Possibilistic Maximum Likelihood (PML) criterion is introduced to improve classification rates compared to a classical approach using only information from hard sources. The proposed PML allows to jointly exploit both probabilistic and possibilistic sources within the same probabilistic decision-making framework, without imposing to convert the possibilistic sources into probabilistic ones, and vice versa.

In this paper, we inspect the range and azimuthal defocusing of a buried point scatterer for a low frequency (L or P band) synthetic aperture radar (SAR). Firstly we present the hydraulic electromagnetic and propagation models that allow us to calculate the water content profile in the soil, to deduce the dielectric constant and to simulate the electromagnetic (EM) wave propagation and attenuation. Secondly results of pulse simulation and focusing for several configurations (band polarization, incident angle, point scatterer depth, water content profile) are presented and discussed. The advantages and drawbacks of L and P radars are discussed in the conclusion.