Building a Parallel Universe Image Synthesis from Land Cover Maps and Auxiliary Raster Data

Gerald Baier, Antonin Deschemps, Michael Schmitt, Naoto Yokoya

We synthesize both optical RGB and SAR remote sensing images from land cover maps and auxiliary raster data using GANs. In remote sensing many types of data, such as digital elevation models or precipitation maps, are often not reflected in land cover maps but still influence image content or structure. Including such data in the synthesis process increases the quality of the generated images and exerts more control on their characteristics. Our method fuses both inputs by spatially adaptive normalization layers, previously published as SPADE semantic image synthesis. In contrast to SPADE, these normalization layers are applied to a full-blown generator architecture consisting of encoder and decoder, to take full advantage of the information content in the auxiliary raster data. Our method successfully synthesizes medium (10m) and high (1m) resolution images, when trained with the corresponding dataset. We show the advantage of data fusion of land cover maps and auxiliary information using mean intersection over union, pixel accuracy and FID using pre-trained U-Net segmentation models. Handpicked images exemplify how fusing information avoids ambiguities in the synthesized images. By slightly editing the input our method can be used to synthesize realistic changes, i.e., raising the water levels. The source code is available at https://github.com/gbaier/rs_img_synth and we published the newly created high-resolution dataset at https://ieee-dataport.org/open-access/geonrw.

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