  This paper focuses on two main issues; first one is the impact of combination
of multi-sensor images on the supervised learning classification accuracy using
segment Fusion (SF). The second issue attempts to undertake the study of
supervised machine learning classification technique of remote sensing images
by using four classifiers like Parallelepiped (Pp), Mahalanobis Distance (MD),
Maximum-Likelihood (ML) and Euclidean Distance(ED) classifiers, and their
accuracies have been evaluated on their respected classification to choose the
best technique for classification of remote sensing images. QuickBird
multispectral data (MS) and panchromatic data (PAN) have been used in this
study to demonstrate the enhancement and accuracy assessment of fused image
over the original images using ALwassaiProcess software. According to
experimental result of this study, is that the test results indicate the
supervised classification results of fusion image, which generated better than
the MS did. As well as the result with Euclidean classifier is robust and
provides better results than the other classifiers do, despite of the popular
belief that the maximum-likelihood classifier is the most accurate classifier.
