  We investigate applying convolutional neural network (CNN) architecture to
facilitate aerial hyperspectral scene understanding and present a new
hyperspectral dataset-AeroRIT-that is large enough for CNN training. To date
the majority of hyperspectral airborne have been confined to various
sub-categories of vegetation and roads and this scene introduces two new
categories: buildings and cars. To the best of our knowledge, this is the first
comprehensive large-scale hyperspectral scene with nearly seven million pixel
annotations for identifying cars, roads, and buildings. We compare the
performance of three popular architectures - SegNet, U-Net, and Res-U-Net, for
scene understanding and object identification via the task of dense semantic
segmentation to establish a benchmark for the scene. To further strengthen the
network, we add squeeze and excitation blocks for better channel interactions
and use self-supervised learning for better encoder initialization. Aerial
hyperspectral image analysis has been restricted to small datasets with limited
train/test splits capabilities and we believe that AeroRIT will help advance
the research in the field with a more complex object distribution to perform
well on. The full dataset, with flight lines in radiance and reflectance
domain, is available for download at https://github.com/aneesh3108/AeroRIT.
This dataset is the first step towards developing robust algorithms for
hyperspectral airborne sensing that can robustly perform advanced tasks like
vehicle tracking and occlusion handling.
