PT Unknown
AU Patricia Suarez
   Angel Sappa
   Boris X. Vintimilla
TI Vegetation Index Estimation from Monospectral Images
BT 15th International Conference on Images Analysis and Recognition
PY 2018
BP 353
EP 362
VL 10882
AB This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhancedetails, resulting in a sharp NDVI image. Then, the discriminative modelestimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index.
ER