ASTER GDEM and SRTM1 Elevation Data Integration with RProp Neural Network

  Objectives  There exist some differences between the elevation accuracy of advanced spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM) and shuttle radar topography mission (SRTM) data, because their methods of data acquisition and post-processing are different.

  Methods  To improve the DEM accuracy by taking advantages of ASTER GDEM and SRTM, the resilient backpropagation (RProp) neural network algorithm is adopted to integrate them. First, we use two units in loess hilly gully topography as test areas to construct models and validate their effects. Second, we take 1∶10 000 DEM as the reference data to construct the corrected ASTER GDEM elevation model, the corrected SRTM1 elevation model and the integrated elevation model of ASTER GDEM and SRTM1 by RProp neural network algorithm. Meanwhile, the integrated elevation model of ASTER GDEM and SRTM1 is created with back propagation (BP) neural network. Then, the optimized effects of elevation precision of these models are analyzed. Finally, the model integrated by RProp is tested in the validation site.

  Results  The results show that the model integrated by RProp is better than the corrected ASTER GDEM elevation model, the corrected SRTM1 elevation model, and the model integrated by BP neural network, and its elevation deviation is reduced by 6.81 m, 0.34 m, 0.19 m, respectively.

  Conclusions  It confirms that the model integrated by RProp has good applicability and error correction effect.