Volume IV-1 | Copyright
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 141-146, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Sep 2018

26 Sep 2018


M. Schmitt1, L. H. Hughes1, and X. X. Zhu1,2 M. Schmitt et al.
  • 1Signal Processing in Earth Observation, Technical University of Munich (TUM), Munich, Germany
  • 2Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, Germany

Keywords: Synthetic aperture radar (SAR), optical remote sensing, Sentinel-1, Sentinel-2, deep learning, data fusion

Abstract. While deep learning techniques have an increasing impact on many technical fields, gathering sufficient amounts of training data is a challenging problem in remote sensing. In particular, this holds for applications involving data from multiple sensors with heterogeneous characteristics. One example for that is the fusion of synthetic aperture radar (SAR) data and optical imagery. With this paper, we publish the SEN1-2 dataset to foster deep learning research in SAR-optical data fusion. SEN1-2 comprises 282;384 pairs of corresponding image patches, collected from across the globe and throughout all meteorological seasons. Besides a detailed description of the dataset, we show exemplary results for several possible applications, such as SAR image colorization, SAR-optical image matching, and creation of artificial optical images from SAR input data. Since SEN1-2 is the first large open dataset of this kind, we believe it will support further developments in the field of deep learning for remote sensing as well as multi-sensor data fusion.

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