Fusion of TanDEM-X and Cartosat-1 elevation data supported by neural network-predicted weight maps

Abstract

This is the pre-acceptance version, to read the final version, please go to ISPRS Journal of Photogrammetry and Remote Sensing on ScienceDirect. 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 highresolution 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) of DEMs is improved by up to 50% in urban areas and 22% in non-urban areas while the improvement by the HEM-based method does not exceed 20% and 10% in urban and non-urban areas respectively.

Assessment of Vertical Accuracy for TanDEM-X 90 m DEMs in Plain, Moderate, and Rugged Terrain

“…The variability in the quality of openly accessible DEMs suggests that these openly accessible DEMs should be utilized cautiously as per the application requirements. New techniques also demand correct selection of input DEMs, while executing DEM fusion or generation of superresolution DEMs providing an opportunity to improve DEMs [10,11,17].…”

“…that create ideal conditions for remote sensing [7]. TanDEM-X has been used for applications like extraction of digital building height models [8], archaeological sites [9], DEM fusion using ANN techniques [10] and DEM super-resolution [11]. AW3D30 was found to be the most promising while investigating the performances of seven public freely-accessed DEM datasets (ASTER GDEM V2, SRTM-3 V4.1 DEM, SRTM-1 DEM, AW3D30 DEM, VFP-DEM, MERIT DEM, Seamless SRTM-1 DEM) over the HMA region (Hengduan Mountains and Himalayas) by referring to high-accuracy elevation data from ICESat altimetry [12].…”

Fusion of Multi-Sensor-Derived Heights and OSM-Derived Building Footprints for Urban 3D Reconstruction

“…In more detail, first the absolute accuracy of Cartosat-1 is increased to the level of absolute accuracy of the TanDEM-X DEM by vertical alignment. Next, both DEMs can be integrated using a sophisticated approach presented in our previous research [25]. The fusion method is developed for multi-sensor DEM fusion with the support of neural-network-predicted fusion weights.…”

Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling

“…Although both these methods were applied successfully to building an error regression model, they were limited to forested areas and/or coarse resolution, which do not represent the dense urban areas. Bagheri et al (2018) fused two different sets of DEM data (TanDEM-X and Cartosat-1) using ANN to enhance the quality of both DEM datasets [18]. The authors trained an ANN to learn the pattern of the relationship between height errors and features from the two datasets.…”