STATISTIC TESTS AIDED MULTI-SOURCE DEM FUSION

• Main Authors: C. Y. Fu, J. R. Tsay
• Format: Article
• Language: English
• Published: Copernicus Publications 2016-06-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B6/227/2016/isprs-archives-XLI-B6-227-2016.pdf
• ISSN: 1682-1750; 2194-9034

Since the land surface has been changing naturally or manually, DEMs have to be updated continually to satisfy applications using the latest DEM at present. However, the cost of wide-area DEM production is too high. DEMs, which cover the same area but have different quality, grid sizes, generation time or production methods, are called as multi-source DEMs. It provides a solution to fuse multi-source DEMs for low cost DEM updating. The coverage of DEM has to be classified according to slope and visibility in advance, because the precisions of DEM grid points in different areas with different slopes and visibilities are not the same. Next, difference DEM (dDEM) is computed by subtracting two DEMs. It is assumed that dDEM, which only contains random error, obeys normal distribution. Therefore, student test is implemented for blunder detection and three kinds of rejected grid points are generated. First kind of rejected grid points is blunder points and has to be eliminated. Another one is the ones in change areas, where the latest data are regarded as their fusion result. Moreover, the DEM grid points of type I error are correct data and have to be reserved for fusion. The experiment result shows that using DEMs with terrain classification can obtain better blunder detection result. A proper setting of significant levels (α) can detect real blunders without creating too many type I errors. Weighting averaging is chosen as DEM fusion algorithm. The priori precisions estimated by our national DEM production guideline are applied to define weights. Fisher’s test is implemented to prove that the priori precisions correspond to the RMSEs of blunder detection result.