170 citations · 357 across the 11 of their papers we have counts for
7 papers · 1 filter
Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution
Tzu-Hsin Karen Chen, Chunping Qiu, Michael Schmitt +3
Landsat imagery is an unparalleled freely available data source that allows reconstructing horizontal and vertical urban form. This paper addresses the challenge of using Landsat d…
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
Chunping Qiu, Xiaochong Tong, Michael Schmitt +2
As a unique classification scheme for urban forms and functions, the local climate zone (LCZ) system provides essential general information for any studies related to urban environ…
Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection
Michael Schmitt, Gerald Baier, Xiao Xiang Zhu
This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pair…
Fusion of TanDEM-X and Cartosat-1 Elevation Data Supported by NeuralNetwork-Predicted Weight Maps
Hossein Bagheri, Michael Schmitt, Xiao Xiang Zhu
Recently, the bistatic SAR interferometry mission TanDEM-X provided a global terrain map with unprecedented accuracy. However, visual inspection and empirical assessment of TanDEM-…
A Framework for SAR-Optical Stereogrammetry over Urban Areas
Hossein Bagheri, Michael Schmitt, Pablo d'Angelo +1
Currently, numerous remote sensing satellites provide a huge volume of diverse earth observation data. As these data show different features regarding resolution, accuracy, coverag…
Fusion of Urban TanDEM-X raw DEMs using variational models
Hossein Bagheri, Michael Schmitt, Xiao Xiang Zhu
Recently, a new global Digital Elevation Model (DEM) with pixel spacing of 0.4 arcseconds and relative height accuracy finer than 2m for flat areas (slopes < 20%) and better than 4…