Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery

Arthita Ghosh, Max Ehrlich, Sohil Shah, Larry S. Davis, Rama Chellappa

CVPRW 2018 pp. 257-261

doi:10.1109/CVPRW.2018.00047 /cvprw/2018/ghosh2018cvprw-stacked/

Abstract

We present a semantic segmentation algorithm for RGB remote sensing images. Our method is based on the Dilated Stacked U-Nets architecture. This state-of-the-art method has been shown to have good performance in other applications. We perform additional post-processing by blending image tiles and degridding the result. Our method gives competitive results on the DeepGlobe dataset.

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Cite

Text

Ghosh et al. "Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00047

Markdown

[Ghosh et al. "Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/ghosh2018cvprw-stacked/) doi:10.1109/CVPRW.2018.00047

BibTeX

@inproceedings{ghosh2018cvprw-stacked,
  title     = {{Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery}},
  author    = {Ghosh, Arthita and Ehrlich, Max and Shah, Sohil and Davis, Larry S. and Chellappa, Rama},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2018},
  pages     = {257-261},
  doi       = {10.1109/CVPRW.2018.00047},
  url       = {https://mlanthology.org/cvprw/2018/ghosh2018cvprw-stacked/}
}