AeroRIT: A New Scene for Hyperspectral Image Analysis

We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral data set, AeroRIT, which is large enough for CNN training. To date, the majority of hyperspectral airborne has been confined to various subcategories 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 the 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 data sets 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 data set, with flight lines in radiance and reflectance domains, is available for download at https://github.com/aneesh3108/AeroRIT. This data set is the first step toward developing robust algorithms for hyperspectral airborne sensing that can robustly perform advanced tasks such as vehicle tracking and occlusion handling.