The field of wavelets has opened up new opportunities for the compression of satellite sensor imagery. This paper examines the influence of wavelet compression on the automatic classification of urban environments. Airborne laser scanning data is introduced as an additional channel alongside the spectral channels of color infrared imagery. This effectively integrates the local height and multi-spectral information sources. To incorporate context information, the feature base is expanded to include both spectral and non-spectral features. A maximum likelihood classification approach is then applied. It is demonstrated that the classification of urban scenes is considerably improved by fusing multi-spectral and geometric data sets. The fused imagery is then systematically compressed (channel by channel) at compression rates ranging from 5 to 100 using a wavelet-based algorithm. The compressed imagery is then classified using the approach described here above. Analysis of the results obtained indicates that a compression rate of up to 20 can conveniently be employed without adversely affecting the segmentation results.