Hyperspectral imaging (HSI) sensors acquire rich spectral information of objects in hundreds of narrow spectral bands, which can be useful in extracting unique features. In recent years, Unmanned Aerial Vehicle (UAV) based HSI techniques are widely used in remote sensing fields due to their wide field of coverages, short revisiting periods, high spectral and spatial resolutions. Pushbroom sensors are line scanners, which acquire data in lines/frames. When a push-broom HSI sensor is used in a UAV platform, the image quality, ground pixel resolution are governed by the UAV and sensor operating parameters, which need to be carefully chosen. In this paper, we propose a mathematical approach for choosing the optimal combination of operating parameters such as UAV speed, flight altitude, sensor frame rate to acquire quality hyperspectral (HS) images with desired ground pixel resolution. Different combinations of camera and flight parameters were tested and evaluated with the classification performance of a convolutional neural network (CNN) model on acquired different vegetation HSI data. We obtained classification accuracies of 96.78%, 97.65%, and 95.55% on HS images acquired from 30m, 40m, and 50m flight altitudes respectively. © 2021 IEEE.