Image Vegetation Index Through a Cycle Generative Adversarial Network

Patricia L. Suarez, Angel D. Sappa, Boris X. Vintimilla, Riad I. Hammoud; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

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.

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[bibtex]
@InProceedings{Suarez_2019_CVPR_Workshops,
author = {Suarez, Patricia L. and Sappa, Angel D. and Vintimilla, Boris X. and Hammoud, Riad I.},
title = {Image Vegetation Index Through a Cycle Generative Adversarial Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}