NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation
Abstract
Semantic Segmentation of satellite images is one of the most challenging problems in computer vision as it requires a model capable of capturing both local and global information at each pixel. Current state of the art methods are based on Fully Convolutional Neural Networks (FCNN) with mostly two main components: an encoder which is a pretrained classification model that gradually reduces the input spatial size and a decoder that transforms the encoder’s feature map into a predicted mask with the original size. We change this conventional architecture to a model that makes use of full resolution information. NU-Net is a deep FCNN that is able to capture wide field of view global information around each pixel while maintaining localized full resolution information throughout the model. We evaluate our model on the Land Cover Classification and Road Extraction tracks in the DeepGlobe competition.
Cite
Text
Samy et al. "NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00050Markdown
[Samy et al. "NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/samy2018cvprw-nunet/) doi:10.1109/CVPRW.2018.00050BibTeX
@inproceedings{samy2018cvprw-nunet,
title = {{NU-Net: Deep Residual Wide Field of View Convolutional Neural Network for Semantic Segmentation}},
author = {Samy, Mohamed and Amer, Karim and Eissa, Kareem and Shaker, Mahmoud and ElHelw, Mohamed},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2018},
pages = {267-271},
doi = {10.1109/CVPRW.2018.00050},
url = {https://mlanthology.org/cvprw/2018/samy2018cvprw-nunet/}
}