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.

The general approach to matching two scenes by a digital computer is usually costly in computations. A match is determined by selecting the position of maximum cross correlation between the window and each possible shift position of the search region. A new approach which is logarithmically efficient is presented in this paper. Its logarithmic efficiency and computational savings will be demonstrated both theoretically and in practical examples. Experimental results are presented for matching an image region corrupted by noise and for matching images from optical and radar sensors. The significance of this approach is that scene matching can be accomplished by the use of a computer even in cases which are difficult for humans or standard correlation techniques, and can be accomplished with greatly reduced computations.

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 computer applications to 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 transformation must be performed on the images to bring the matching elements into one-to-one correspondence. Because of the difference in operating conditions and sensor characteristics, images of the same object taken by two different sensors have different intensity values. A new intensity matching technique based on the Karhunen-Loeve transformation was developed to match the intensity values of one image to those of the other as closely as possible through the use of a digital computer.