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While deep learning techniques have an increasing impact on many technicalfields, gathering sufficient amounts of training data is a challenging problemin remote sensing. In particular, this holds for applications involving datafrom multiple sensors with heterogeneous characteristics. One example for thatis the fusion of synthetic aperture radar (SAR) data and optical imagery. Withthis paper, we publish the SEN1-2 dataset to foster deep learning research inSAR-optical data fusion. SEN1-2 comprises 282,384 pairs of corresponding imagepatches, collected from across the globe and throughout all meteorologicalseasons. Besides a detailed description of the dataset, we show exemplaryresults for several possible applications, such as SAR image colorization,SAR-optical image matching, and creation of artificial optical images from SARinput data. Since SEN1-2 is the first large open dataset of this kind, webelieve it will support further developments in the field of deep learning forremote sensing as well as multi-sensor data fusion.

The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusion