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

Arthita Ghosh, Max Ehrlich, Sohil Shah, Larry S. Davis, Rama Chellappa; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 257-261

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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[pdf]
[bibtex]
@InProceedings{Ghosh_2018_CVPR_Workshops,
author = {Ghosh, Arthita and Ehrlich, Max and Shah, Sohil and Davis, Larry S. and Chellappa, Rama},
title = {Stacked U-Nets for Ground Material Segmentation in Remote Sensing Imagery},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}