THE SEN1-2 DATASET for DEEP LEARNING in SAR-OPTICAL DATA FUSION

M. Schmitt, L. H. Hughes, X. X. Zhu

Research output: Contribution to journalConference articlepeer-review

139 Scopus citations

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.

Original languageEnglish
Pages (from-to)141-146
Number of pages6
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume4
Issue number1
DOIs
StatePublished - 23 Sep 2018
Event2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications - Karlsruhe, Germany
Duration: 10 Oct 201812 Oct 2018

Keywords

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

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