  This paper focuses on two main issues; first one is the impact of Similarity
Search to learning the training sample in metric space, and searching based on
supervised learning classi-fication. In particular, four metrics space
searching are based on spatial information that are introduced as the
following; Cheby-shev Distance (CD); Bray Curtis Distance (BCD); Manhattan
Distance (MD) and Euclidean Distance(ED) classifiers. The second issue
investigates the performance of combination of mul-ti-sensor images on the
supervised learning classification accura-cy. QuickBird multispectral data (MS)
and panchromatic data (PAN) have been used in this study to demonstrate the
enhance-ment and accuracy assessment of fused image over the original images.
The supervised classification results of fusion image generated better than the
MS did. QuickBird and the best results with ED classifier than the other did.
