Gross Error Detection On Digital Elevation Model

The widespread availability of powerful desktop computers, easy-to-use soft ware tools and Geographical Information System (GIS) datasets have raised the quality control problem of input data to be a crucial one. Digital elevation model (DEM), as the essential sources of GIS, has become an important part of a national spatial data infrastructure (SDI) in many countries, under the umbrella of digital geospatial data framework (DGDF) .For any DEM project, accuracy, efficiency, and economy are the three main factors to be considered (Li, 1992) . Accuracy is perhaps the single most important factor to be considered because, if the accuracy of DEM cannot meet the requirements, then the whole project needs to be repeated and the community and efficiency will ultimately be affected. The factors that affect DEM accuracy are various. From the errors point of view, they can be classified into three types: gross errors, systemic errors and random errors. The presence of gross errors will distort the image of the spatial variation present in digital elevation model (DEM) . In some cases, totally undesirable and unacceptable results may be produced in DEM as well as its products due to the existence of such gross errors. Therefore, the detection of gross errors in DEM has been becoming the focus that people are greatly concerned for.The efficient method detecting gross errors in DEM should proceed in initial dataset, which includes grid DEM and irregular DEM. From the view of utility, the progress in grid DEM is comparatively easy, and the corresponding research is abundance. However, if the data points are irregularly distributed, then difficulties in checking the consistency of slope change will be met. It will, therefore, not be applicable in this case. So Dr. Li zhilin presented the algorithm, the point wise method, to detect the gross errors in irregular DEM. Just depending on analyzing the existing methods, three new algorithms, respectively the algorithm based on the relationship of proportion ( namely, the probability that a height point is suspicious of a blunder) , the algorithm based on the mean and median value of bias errors, are established in this paper. Some simulated examples are given by Monte Carlo Method to verify that the methods proposed is more reliable and correct than existing methods. The testing data can be classified into three sorts by terrain type: plain DEM, hill DEM, and mount DEM. For the all-pervading meaning, the previous...