Fusion of TanDEM-X and Cartosat-1 Elevation Data Supported by NeuralNetwork-Predicted Weight Maps

TL;DR

This paper introduces a neural network-based method to predict optimal weights for fusing TanDEM-X and Cartosat-1 elevation data, significantly enhancing DEM accuracy especially in urban regions.

Contribution

It develops a neural network approach to automatically predict weight maps for DEM fusion, improving the quality of combined elevation models over traditional weighted averaging methods.

Findings

DEM fusion with ANN-predicted weights improves accuracy in urban areas by up to 50%.

Relative accuracy to LiDAR data increases significantly with the proposed method.

The approach outperforms traditional methods in both urban and non-urban environments.

Abstract

Recently, the bistatic SAR interferometry mission TanDEM-X provided a global terrain map with unprecedented accuracy. However, visual inspection and empirical assessment of TanDEM-X elevation data against high-resolution ground truth illustrates that the quality of the DEM decreases in urban areas because of SAR-inherent imaging properties. One possible solution for an enhancement of the TanDEM-X DEM quality is to fuse it with other elevation data derived from high-resolution optical stereoscopic imagery, such as that provided by the Cartosat-1 mission. This is usually done by Weighted Averaging (WA) of previously aligned DEM cells. The main contribution of this paper is to develop a method to efficiently predict weight maps in order to achieve optimized fusion results. The prediction is modeled using a fully connected Artificial Neural Network (ANN). The idea of this ANN is to extract suitable features from DEMs that relate to height residuals in training areas and then to automatically learn the pattern of the relationship between height errors and features. The results show the DEM fusion based on the ANN-predicted weights improves the qualities of the study DEMs. Apart from increasing the absolute accuracy of Cartosat-1 DEM by DEM fusion, the relative accuracy (respective to reference LiDAR data) ofDEMs is improved by up to 50% in urban areas and 22% in non-urban areas while the improvement by them-based method does not exceed 20% and 10% in urban and non-urban areas respectively.