Image Vegetation Index Through a Cycle Generative Adversarial Network
Abstract
This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided.
Cite
Text
Suarez et al. "Image Vegetation Index Through a Cycle Generative Adversarial Network." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019. doi:10.1109/CVPRW.2019.00133Markdown
[Suarez et al. "Image Vegetation Index Through a Cycle Generative Adversarial Network." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019.](https://mlanthology.org/cvprw/2019/suarez2019cvprw-image/) doi:10.1109/CVPRW.2019.00133BibTeX
@inproceedings{suarez2019cvprw-image,
title = {{Image Vegetation Index Through a Cycle Generative Adversarial Network}},
author = {Suarez, Patricia L. and Sappa, Ángel D. and Vintimilla, Boris Xavier and Hammoud, Riad I.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2019},
pages = {1014-1021},
doi = {10.1109/CVPRW.2019.00133},
url = {https://mlanthology.org/cvprw/2019/suarez2019cvprw-image/}
}