  Vegetation Indices based on paired images of the visible color spectrum (VIS)
and near infrared spectrum (NIR) have been widely used in remote sensing
applications. These vegetation indices are extended for their application in
autonomous driving in unstructured outdoor environments. In this domain we can
combine traditional vegetation indices like the Normalized Difference
Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) with Convolutional
Neural Networks (CNNs) pre-trained on available VIS datasets. By laying a focus
on learning calibrated CNN outputs, we can provide an approach to fuse known
hand-crafted image features with CNN predictions for different domains as well.
The method is evaluated on a VIS+NIR dataset of semantically annotated images
in unstructured outdoor environments. The dataset is available at
mucar3.de/iros2022-ppniv-tas-nir.
