PT Unknown
AU Patricia Suarez
   Angel Sappa
   Boris X. Vintimilla
   Riad I. Hammoud
TI Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation
BT 28th IEEE International Conference on Image Processing
PY 2021
BP 19
EP 22
DI 10.1109/ICIP42928.2021.9506746
AB This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.
ER