Deep Learning for Hyperspectral Image Classification on Embedded Platforms

Hyperspectral image (HSI) analysis refers to the processes used to identify and classify objects photographed using equipment that can image photons from a broad range of the electromagnetic spectrum. Downlinking such large images from space on radiation-resistant platforms with limited on-board computing power takes a large amount of time, memory, and other mission-critical resources. Performing such analysis in space before downlinking all images will save these resources by enabling a subset of images of interest to be downloaded rather than the entire set. The goal of this study is to benchmark and evaluate HSI-classification methods which incorporate deep learning on embedded platforms with limited computing resources. Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) are the classification methods used in this study. These algorithms were executed on a desktop PC and two embedded platforms: the ODROID-C2 and the Raspberry Pi 3B. Accuracy, run-time, and memory benchmarks determined the optimal model for each platform. Based on results gathered in this research, CNN classification is recommended for the desktop PC due to its high accuracy of 97%. MLP classification is recommended for the embedded platforms under study, as it showcased the shortest run-time and second-highest accuracy.