LEAST SQUARE MATCHING 1 TO COMBINE THE TWO IMAGES OF NCU AREA
Least square matching technique is included in area-based digital matching method. Conceptually, least square matching is closely related to the correlation method, with the added advantage of being able to obtain the match location to a fraction of a pixel. Least square matching (LS1)1 has merit to minimize the sum of squares for grayscale differences, so the result will be more accurate.
The images covering the National Central University (Taiwan) area are aerial images taken from digital camera with sensor ultracam-D. Interior orientation parameter consist of focal length in 101.400000mm, principal point offset (0.000000e+000, 0.000000e+000)mm, and principal point symmetry (-2.110000e-001, 0.000000e+000)mm.
The experimental result shows that the best accuracy of x direction is reached when the rotation angle is 9 degree, then those of y direction is reached when the rotation angle is 3 degree. The accuracy of both directions is getting worse when the scale of image is less than 0.8. The success rate 100% is reached in all of window size except 51 and 101. Then, the best accuracy of x direction is showed in 3x3 window size, those of y direction is employed when the work used the window size 11x11. Based on the experimental result, it can be concluded that using different rotation and scale can get the different result that it will be worse or better. Thus, to get the better result in matching image and better accuracy, the work should use the orthorectified image as base image to do rotation scheme and use small window size to minimize the number iteration, but it will be not significant with RMSe.
Chen, L. C., 2009, The hand book of Digital Photogrammetry course, Center for Space and Remote Sensing Research, National Central University.
Gruen, A., 1985a., Adaptive least squares correlation: a powerful image matching technique. South African Journal of Photog., Remote Sensing and Cartography, 14(3), pp. 175-187.
Masuda, T., 2002. Registration and integration of multiple range images by matching signed distances fields for object shape modeling. Computer Vision and Image Understanding, 87 (1-3), pp. 51-65
Mikhail, E., 1976. Observation and least squares. New York, NY University Press, New York.
Mikhail, E., Bethel, J., and McGlone, J., 2001. Introduction to modern photogrammetry. Join Wiley & Sons Inc., New York.
Wolf, R. P., Dewitt, A., B., 2000, Elements of Photogrammetry With Application in GIS, The University of Wisconsin, Madison
Lillesand, T. M., Kiefer, R. W., Chipman, J.W., 2004, Remote Sensing and Image Interpretation. Wiley : USA
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