  Inferring spectral signatures from ground based natural images has acquired a
lot of interest in applied deep learning. In contrast to the spectra of ground
based images, aerial spectral images have low spatial resolution and suffer
from higher noise interference. In this paper, we train a conditional
adversarial network to learn an inverse mapping from a trichromatic space to 31
spectral bands within 400 to 700 nm. The network is trained on AeroCampus, a
first of its kind aerial hyperspectral dataset. AeroCampus consists of high
spatial resolution color images and low spatial resolution hyperspectral images
(HSI). Color images synthesized from 31 spectral bands are used to train our
network. With a baseline root mean square error of 2.48 on the synthesized RGB
test data, we show that it is possible to generate spectral signatures in
aerial imagery.
