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Area based matching of intensity images is a well known technique applied to solve various photogrammetric tasks like parallax measurement, point transfer, orientation of cameras, DTM reconstruction and others. The intensities of two or more images are the observables of a least squares estimation process which aims at deriving the parameters of a geometric model. For matching two images the most widely used geometric model is an affine mapping between local areas of the image pair. Experimentally verified is the high precision of area based matching which is about 1/10th of the pixel size. Roughly this rule of thumb holds also for the different generalizations of modelling the least squares matching problem including multi- image, object-space oriented, geometrically constrained, and other variations. Up to now only little attention has been given to the extension of the matching model to color or multispectral images. Color is generally considered to be an important clue for identification and recognition processes. The purpose of this paper is to investigate quality differences between an area based matching of color or multichannel images and images with just one channel. The formulation of multichannel image matching is presented by using a vector valued image function. For the experimental investigation aerial color images of two projects are used, one being a RGB image pair and the other being an IR image pair. The main results of this study are that (1) multichannel image matching leads to a precision often very close to that of single channel matching using the red or IR channel, respectively, or even to matching based on an intensity image derived by averaging of three channels and (2) multichannel image matching has a larger convergence radius if small mask sizes are used.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Hahn and Claus Brenner "Color image matching", Proc. SPIE 2572, Remote Sensing and Reconstruction for Three-Dimensional Objects and Scenes, (23 August 1995);

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