Stand density, expressed as the number of trees per unit area, is an important forest management parameter. It is used by foresters to evaluate regeneration, to assess the effect of forest management measures, or as an indicator variable for other stand parameters like age, basal area, and volume. In this work, a new density estimation procedure is proposed based on wavelet analysis of very high resolution optical imagery. Wavelet coefficients are related to reference densities on a per segment basis, using an artificial neural network. The method was evaluated on artificial imagery and two very high resolution datasets covering forests in Heverlee, Belgium and Les Beaux de Provence, France. Whenever possible, the method was compared with the well-known local maximum filter. Results show good correspondence between predicted and true stand densities. The average absolute error and the correlation between predicted and true density was 149 trees/ha and 0.91 for the artificial dataset, 100 trees/ha and 0.85 for the Heverlee site, and 49 trees/ha and 0.78 for the Les Beaux de Provence site. The local maximum filter consistently yielded lower accuracies, as it is essentially a tree localization tool, rather than a density estimator.