The analysis of classified hemispherical images of forest stands is a common method in forestry to determine site-related factors for plants. A segmentation of hemispheric images, followed by an intersection with the daily and annual path of the sun, provides a quantitative measure on the solar radiation conditions for young plants. For that purpose reliably classified images have to be generated. The paper describes a method for the sub-pixel classification of hemispherical canopy images. The goal was to develop an automatic method which allows classifying canopy images independent of the type and density of the forest stand and the sky cover. The procedure can be divided into two steps. First the pixels that purely represent the classes sky or vegetation are determined. This is done by analysing radial grey value profiles corresponding to the homogeneity of neighbouring pixels. Inhomogeneous regions usually define border pixels between the two classes. In addition to this texture criterion the multi-spectral information is considered insofar as a pixel can be identified unambiguously as a pure vegetation pixel by its RGB-values. In a second step the remaining unclassified mixed pixels are assigned to these two classes. For that purpose the percentage ratio of each class is determined for each mixed pixel.