Channel estimation, equalisation, and evaluation for high-mobility airborne hyperspectral data transmission

In the past few years, unmanned aerial vehicles (UAVs) have become a primary airborne platform for hyperspectral imager for studies on precision agriculture, defence, and the environment. The ‘push-broom’ type of hyperspectral sensors require moving vehicle, and transmission and analysis of hyperspectral data by means of a UAV's high-mobility channel is challenging. While high bandwidth of hyperspectral imaging justify using orthogonal frequency division multiplexing (OFDM) for data transmission, the high speed of UAVs imposes intercarrier interference (ICI) on the transmitted OFDM signal because of the Doppler shift. This study proposes a technique for channel estimation and equalisation in order to compensate the ICI. This technique uses a complete channel matrix estimation in the frequency domain in contrast to conventional methods that only use diagonal elements when recovering the data. In order to evaluate the received data using this technique, a classification framework was designed that took into consideration both spectral and spatial information. In order to verify the robustness of the proposed model, the system was analysed using a Pavia Center hyperspectral dataset, and evaluated against speeds of 50 and 500 m/s. By using this method, improvement in both data transmission and the analysis was achieved.