DEM reconstruction

P.M. Buscema, G. Massini, M. Fabrizi, M. Breda, F. Della Torre
The ANNS approach to DEM reconstruction
in Computational Intelligence, Wiley, DOI: 10.1111/coin.12151, November 2017.


This research has 6 fundamental aims: (i) to present a  modified version of Taylor’s interpolation, one that is more effective and faster than the original; (ii) outline the capability of artificial neural networks (ANNs) to perform an optimal functional approximation of the digital elevation model reconstruction from a satellite map, using a small and independent sample of Global Positioning System observations; (iii) demonstrate experimentally how ANNs outperform the traditional and most used algorithm for the height interpolation (Taylor’s interpolation); (iv) introduce a new ANN, the Conic Net, able to outperform the results of the classic and more known multilayer perceptron; (v) determine that Conic Nets, even when using Taylor’s modified interpolation as input features, are able to optimally approximate the heights with one order of magnitude more than the original satellite map; and (vi) make evident the possibility to interpolate the DEM heights through an ANN, which learns a data set of known points.