Efficient representation of digital terrain models: compression and spatial decorrelation techniques
Points on terrain surfaces often are correlated, i.e., the height of one point is dependent on the height of its neighbouring points. Since this correlation to some degree represents redundant information, it can be utilized in the construction of digital terrain models by reducing the amount of data volume. The topic for the present article is to compare the spatial decorrelation properties of wavelets with adaptive triangulation and an adaptive application of universal kriging. We have defined spatial decorrelation as the separation of the structural information of a terrain surface from its local fluctuations. The wavelet method we have selected for our study, is a 2D wavelet transform which reproduces polynomials of degree two. The experiments show that wavelets have slightly better decorrelation properties than the two other methods. The results also show that wavelets have many attractive properties in terrain modelling in terms of model generalization, noise reduction, multi-resolution, low time complexity and strong numerical stability.
wavelets kriging noise reduction adaptive terrain modelling multi-resolution