  The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the
multispectral remote sensing imagery provides vital information for the
landcover classification, especially concerning the vegetation assessment.
Despite the usefulness of NIR, common RGB is not always accompanied by it.
Modern achievements in image processing via deep neural networks allow
generating artificial spectral information, such as for the image colorization
problem. In this research, we aim to investigate whether this approach can
produce not only visually similar images but also an artificial spectral band
that can improve the performance of computer vision algorithms for solving
remote sensing tasks. We study the generative adversarial network (GAN)
approach in the task of the NIR band generation using just RGB channels of
high-resolution satellite imagery. We evaluate the impact of a generated
channel on the model performance for solving the forest segmentation task. Our
results show an increase in model accuracy when using generated NIR comparing
to the baseline model that uses only RGB (0.947 and 0.914 F1-score
accordingly). Conducted study shows the advantages of generating the extra band
and its implementation in applied challenges reducing the required amount of
labeled data.
