The problem of matching two images of the same scene, taken by different sensors under different viewing geometries, is a challenging problem in the field of image processing and pattern recognition. The scenes are usually transformed so drastically by the different viewing geometries and sensor characteristics that it is extremely difficult, if not impossible, to match the original images without the proper data processing. Geometric and intensity transformations must be performed to bring the matching elements and their intensity into a one-to-one correspondence. Objects of interest represented by subimages of one scene were located in the other using scene matching techniques with intensity difference and edge features as measurement features. Performance characteristics of the matches by these techniques are presented in terms of the probability of a match as a function of the probability of false fix.