Kavli Affiliate: Jia Liu
| First 5 Authors: Donghui Li, Jia Liu, Fang Liu, Wenhua Zhang, Andi Zhang
| Summary:
Deep learning based semantic segmentation is one of the popular methods in
remote sensing image segmentation. In this paper, a network based on the widely
used encoderdecoder architecture is proposed to accomplish the synthetic
aperture radar (SAR) images segmentation. With the better representation
capability of optical images, we propose to enrich SAR images with generated
optical images via the generative adversative network (GAN) trained by numerous
SAR and optical images. These optical images can be used as expansions of
original SAR images, thus ensuring robust result of segmentation. Then the
optical images generated by the GAN are stitched together with the
corresponding real images. An attention module following the stitched data is
used to strengthen the representation of the objects. Experiments indicate that
our method is efficient compared to other commonly used methods
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