Tracking in Aerial Hyperspectral Videos Using Deep Kernelized Correlation Filters

Hyperspectral imaging holds enormous potential to improve the state of the art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multimodal hyperspectral sensors have attracted growing interest due to their ability to record extended data quickly from aerial platforms. In this paper, we apply popular concepts from traditional object tracking, namely, kernelized correlation filters (KCFs) and deep convolutional neural network features to aerial tracking in the hyperspectral domain. We propose the deep hyperspectral KCF-based tracker (DeepHKCF) to efficiently track aerial vehicles using an adaptive multimodal 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. In addition, we generate a large, synthetic, single-channel data set using DIRSIG to perform vehicle classification in the wide-area motion imagery (WAMI) platform. This way, the high fidelity of the DIRSIG software is proven, and a large-scale aerial vehicle classification data set is released to support studies on vehicle detection and tracking in the WAMI platform.