Detection of Gross Errors in DEM Based on Principal Components Analysis

To probe into the effect of error spatial distribution on the detection rate of gross error for a digital elevation model(DEM),two different models,spike-like and pyramid-like gross error models,were constructed.Data with different gross error rates of 0.2% to 3.0% were simulated and added to DEM randomly.A detection algorithm based on the principal components analysis(PCA) was used for tests.The result shows that whether gross errors are spatially correlated or not,the detection rate decreases with increasing of the gross error rate.To a spike-like gross error,when the rate is below 1.0%,almost all gross errors can be found out.While to a pyramid-like gross error,the detection efficiency decreases to 50% when the rate is equal to 1.0%.It can be found that the spatial relativity and a high gross error rate will cut down the detection rate of the algorithm.