Effect of Wavelet Compression on the Automatic Classification of Urban Environments Using High Resolution Multispectral Imagery and Laser Scanning Data

This paper examines the influence of data fusion and wavelet compression on the automatic classification of urban environments. The principal data used is airborne Daedalus scanner imagery. Laser scanning data is introduced as an additional channel alongside the spectral channels thus effectively fusing the local height and multispectral information. The feature base is expanded to include both spectral (e.g., spectral signature and texture) and spatial features (e.g., shape, size, topology etc.). This enables the incorporation of context information into the feature extraction. A maximum likelihood classification is then applied. It is demonstrated that the classification of urban scenes is significantly improved by integrating multispectral and geometric datasets. The fused imagery is then systematically compressed (channel by channel) at compression rates ranging from 5 to 100 using a wavelet-based compression algorithm. The compressed imagery is then classified. Analysis of the results obtained indicates that a compression rate of up to 20 can conveniently be employed without adversely affecting the segmentation results.