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

Abstract

The field of wavelets has opened up new opportunities for the compression of satellite data. This paper examines the influence of data compression on the automatic classification of urban environments. Data from Daedalus airborne scanner imagery is used. Laser scanning altitude data is introduced as an additional channel alongside the spectral channels thus effectively integrating the local height and multispectral information sources. In order to incorporate context information, the feature base is expanded to include both spectral and non-spectral features. A maximum likelihood classification is then applied. It is demonstrated that the classification of urban scenes is considerably improved by fusing multispectral 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 compression algorithm. The compressed imagery is then classified using the approach described hereabove. Analysis of the results obtained indicates that a compression rate of up to 20 can conveniently be employed without adversely affecting the segmentation results.