  Hyperspectral imaging holds enormous potential to improve the
state-of-the-art in aerial vehicle tracking with low spatial and temporal
resolutions. Recently, adaptive multi-modal hyperspectral sensors have
attracted growing interest due to their ability to record extended data quickly
from aerial platforms. In this study, we apply popular concepts from
traditional object tracking, namely (1) Kernelized Correlation Filters (KCF)
and (2) Deep Convolutional Neural Network (CNN) features to aerial tracking in
hyperspectral domain. We propose the Deep Hyperspectral Kernelized Correlation
Filter based tracker (DeepHKCF) to efficiently track aerial vehicles using an
adaptive multi-modal hyperspectral sensor. We address low temporal resolution
by designing a single KCF-in-multiple Regions-of-Interest (ROIs) approach to
cover a reasonably large area. To increase the speed of deep convolutional
features extraction from multiple ROIs, we design an effective ROI mapping
strategy. The proposed tracker also provides flexibility to couple with the
more advanced correlation filter trackers. The DeepHKCF tracker performs
exceptionally well with deep features set up in a synthetic hyperspectral video
generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG)
software. Additionally, we generate a large, synthetic, single-channel dataset
using DIRSIG to perform vehicle classification in the Wide Area Motion Imagery
(WAMI) platform. This way, the high-fidelity of the DIRSIG software is proved
and a large scale aerial vehicle classification dataset is released to support
studies on vehicle detection and tracking in the WAMI platform.
