Evaluation of merging SPOT multispectral and panchromatic data for classification of urban environment

Two merging techniques based on the Price algorithm and a high pass filter (HPF) have been used to merge SPOT XS with PAN data to evaluate the effect of merging on classification of an urban area. The Price algorithm gave smaller distortion of spectral characteristics of merged data compared to the HPF algorithm and provided accuracies similar to the original data using spectral or a combination of spectral and texture features. The gain in overall accuracy by adding one texture band over pure spectral features is higher for merged data than with the original data. Both data merging algorithms result in inferior overall and individual class accuracies compared to the original data for pure spectral as well as for a combination of spectral and texture features. The Price algorithm should be used for those spectral bands only, which are highly correlated with the PAN data. For the poorly correlated band, the HPF algorithm should be used