316 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B2. Amsterdam 2000. IMAGE COMPRESSION VERSUS MATCHING ACCURACY

The improvements of aerial image scanners regarding the scan resolution and in particular the development of new digital cameras (e.g. the DMC of Z/I Imaging) comes along with the production of Gigabytes of image data. To handle these big data sets image compression algorithms are applied. The number of developed image compression procedures is growing and investigations of the consequences for photogrammetric image processing are becoming very important for users of compressed image data. This paper investigates theoretically and experimentally the impact of different compression algorithms on image matching quality. The focus is laid on automated point transfer using the area based least squares matching principle. Other automated processes like aerial triangulation, DTM acquisition and relative orientation often directly rely on the results of the employed point transfer process thus consequences on these processes are inferable. In the experimental investigations simulated stereo imagery and real stereo image pairs are taken into account. Representative algorithms of three different compression methods, the JPEG, Wavelet and Fractal compression, are used to verify and quantify the theoretically found relation between matching accuracy and compression ratio. Regarding the peak-signal-to-noise-ratio (PSNR) of the images induced by compression, Wavelet compression proves to be superior to JPEG and Fractal compression. With respect to the success rate and matching accuracy of point transfer a continuous decrease is to b