TY  - JOUR
A1  - Müller, Konstantin
A1  - Leppich, Robert
A1  - Geiß, Christian
A1  - Borst, Vanessa
A1  - Pelizari, Patrick Aravena
A1  - Kounev, Samuel
A1  - Taubenböck, Hannes
T1  - Deep neural network regression for normalized digital surface model generation with Sentinel-2 imagery
JF  - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
N2  - In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%.
KW  - Deep learning
KW  - multiscale encoder
KW  - sentinel
KW  - surface model
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-349424
SN  - 1939-1404
VL  - 16
ER  -