High-Resolution Aerial Image Labeling With Convolutional Neural Networks

@article{Maggiori2017HighResolutionAI,
  title={High-Resolution Aerial Image Labeling With Convolutional Neural Networks},
  author={Emmanuel Maggiori and Yuliya Tarabalka and Guillaume Charpiat and Pierre Alliez},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2017},
  volume={55},
  pages={7092-7103}
}
The problem of dense semantic labeling consists in assigning semantic labels to every pixel in an image. In the context of aerial image analysis, it is particularly important to yield high-resolution outputs. In order to use convolutional neural networks (CNNs) for this task, it is required to design new specific architectures to provide fine-grained classification maps. Many dense semantic labeling CNNs have been recently proposed. Our first contribution is an in-depth analysis of these… CONTINUE READING
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References

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Showing 1-10 of 35 references

Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks

IEEE Transactions on Geoscience and Remote Sensing • 2017
View 12 Excerpts
Highly Influenced

Fully Convolutional Networks for Semantic Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2015
View 9 Excerpts
Highly Influenced

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2017
View 2 Excerpts

HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) • 2016
View 1 Excerpt

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