Hyperspectral imagery generates huge data volumes, consisting of hundreds of contiguous and often highly redundant spectral bands. Difficulties are caused by this high dimensionality. Feature selection (FS) is a possible strategy to reduce the number of bands, consisting in selecting the most relevant bands for a classification problem. It is adapted to the design of superspectral sensor dedicated to specific applications. FS is an optimization problem involving both a metric (that is to say a FS score or criterion measuring the relevance of feature subsets) to optimize and an optimization strategy. In this paper, a wrapper FS score based on Random Forests (RF) and taking into account RF class membership measures was proposed. It was compared to a state-of-the-art wrapper FS score (classification Kappa obtained by RF). Both were then evaluated quantitatively considering both classification performance reached applying different classifiers. An qualitative analysis was also performed to consider the stability/ regularity of the selected features along the spectrum. Even though the quantitative evaluation showed little differences between the two tested FS criteria, there seemed to be a trend in favour of the proposed criterion. Taking into account the measures of class membership provided by a RF classifier slightly improved results, regularizing feature selection.